Covid19Japan.com

Covid19 Japanが独自に収集している陽性者単位のデータ（個票データ）。ソースとデータは全てGitHubにて公開されており、データはJSON形式。「レコード数 \(\neq\) 累計陽性者数」であることに注意。

　

Import

Covid19 JapanがGitHubで公開しているデータは前述のようにJSON形式であり、最新データはlatest.jsonファイルで示されている。このため、読み込む際はひと工夫必要。

個票データ（Patient Data）

陽性者単位の個票データ。

path <- "https://raw.githubusercontent.com/reustle/covid19japan-data/master/docs/patient_data/"

df <- path %>% 
  paste0("latest.json") %>% 
  readr::read_lines() %>% 
  paste0(path, .) %>% 
  jsonlite::fromJSON()

df

　

集計データ（Summary Data）

死亡者数や重症者数などの推移データはsummaryフォルダ内のJSON形式ファイルにまとめられている。読み込むと分かるがリスト型で、その中データフレームが含まれる形式である。
summaryフォルダの他にsummary_minフォルダというフォルダがあるが、summary_minフォルダ内のJSONファイルは単に改行を省略して小さくしたファイル。

path <- "https://raw.githubusercontent.com/reustle/covid19japan-data/master/docs/summary/"

df_s <- path %>% 
  paste0("latest.json") %>% 
  readr::read_lines() %>% 
  paste0(path, .) %>% 
  jsonlite::fromJSON()

df_s %>% summary()

##             Length Class      Mode     
## prefectures 27     data.frame list     
## regions     12     data.frame list     
## daily       37     data.frame list     
## updated      1     -none-     character

　
三つのデータフレームと一つのベクトル（更新日時）から構成されている。データフレームは上から順に都道府県別、地方別、日次となっているが、Lengthを見てわかるようにそれぞれに含まれる集計データが異なっている。

　

都道府県単位集計

更新日時（$updated）における都道府県単位での累積値。厚生労働省がオープンデータから除いている空港検疫・ダイヤモンドプリンセス・長崎クルーズ船・その他が含まれるので全51区分になっている。

df_s$prefectures

陽性者・死亡者などの時系列集計データがネストされて格納されている。日付はネストされていないので、各項目に対するstartDateの項を参照すること。

項目	内容	備考
dailyConfirmedCount	陽性者数	単日
dailyConfirmedStartDate	陽性者数のカウント開始日	区分により開始日が異なる
dailyDeceasedCount	死亡者数	単日
dailyDeceasedStartDate	死亡者数のカウント開始日	区分により開始日が異なる
dailyRecoveredCumulative	快復者数	累計
dailyRecoveredStartDate	快復者数のカウント開始日	区分により開始日が異なる
dailyActive	治療者数1	単日
dailyActiveStartDate	治療者数のカウント開始日	区分により開始日が異なる

¹ 陽性者数から死亡者数と快復者数を引いた数値を治療者数としている

　

地方単位集計

更新日次時点における地方区分単位での累積値。陽性者の時系列集計データが都道府県単位データと同様にネストで格納されているが、死亡者・快復者・治療者のデータは含まれていない。
なお、時系列データの合計値と累積項の値が一致しない場合がある。

df_s$regions

df_s$regions$confirmed[1]

## [1] 64055

df_s$regions$dailyConfirmedCount[[1]] %>% sum()

## [1] 70255

　

日次集計

個票データを日次で集計したもの。日付を見れば分かる通り暗黙の欠落を含んでいる。

df_s$daily

　

更新日時

集計データの更新日時。

df_s$updated

## [1] "2020-11-20T18:51:47+09:00"

　

Area Data

地域・地方ごとの分析を行う場合に便利な都道府県データを用意した。このデータはGistで公開している。

　

Others

病床データ

新型コロナウイルス対策病床オープンデータのデータも用意しておく。

if (googlesheets4::gs4_has_token()) {
beds_by_pref <- "https://docs.google.com/spreadsheets/d/1u0Ul8TgJDqoZMnqFrILyXzTHvuHMht1El7wDZeVrpp8" %>% 
  googlesheets4::read_sheet() %>% 
  dplyr::arrange(dplyr::desc(`発表日`)) %>% 
  dplyr::distinct(`自治体名`, .keep_all = TRUE) %>% 
  dplyr::rename(pref = `自治体名`, beds = `新型コロナウイルス対策感染症病床数`,
                date = `発表日`) %>% 
  dplyr::mutate(beds = as.integer(beds), date = lubridate::as_date(date))
beds_by_pref
}

COVID-19 対策ダッシュボード

NECソリューションイノベータによる都道府県単位の単日集計データ。治療に関する集計データが含まれている。ただし、項目によっては累積値（累計値）のものもある。

"https://covid-19.nec-solutioninnovators.com/download/japan_covid19.csv" %>% 
  readr::read_csv(guess_max = 12500) %>% 
  dplyr::select(date = `公表_年月日`, pref = `都道府県名`,
                confirmed = `PCR検査陽性者`, pcr = `PCR検査実施人数`,
                `入院治療等を要する者`, `うち重症`, `退院又は療養解除となった者の数`,
                `確認中`) %>% 
  dplyr::mutate(date = lubridate::as_date(date)) %>% 
  dplyr::mutate_if(is.numeric, .funs = as.integer)

　

Summarize

最初に個票データの内容を確認する。これには要約に便利なskimrパッケージを用いる。

df %>% 
  skimr::skim()

Data summary

Name	Piped data
Number of rows	130058
Number of columns	23
_______________________
Column type frequency:
character	19
logical	3
numeric	1
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
patientId	0	1.00	1	8	0	128249	0
dateAnnounced	0	1.00	10	10	0	297	0
gender	29393	0.77	1	1	0	2	0
detectedPrefecture	0	1.00	3	15	0	49	0
patientStatus	125959	0.03	8	23	0	8	0
notes	69632	0.46	1	270	0	57520	1
mhlwPatientNumber	129609	0.00	1	11	0	434	0
prefecturePatientNumber	25880	0.80	5	20	0	104169	0
prefectureSourceURL	98687	0.24	5	224	0	3452	0
residence	37681	0.71	1	38	0	1427	0
sourceURL	1130	0.99	1	239	0	8733	0
relatedPatients	118602	0.09	2	259	0	6854	0
knownCluster	127545	0.02	3	88	0	232	0
detectedCityTown	102595	0.21	2	22	0	667	0
cityPrefectureNumber	102813	0.21	1	34	0	27236	2
citySourceURL	118081	0.09	9	317	0	3668	0
deceasedDate	128118	0.01	10	10	0	247	0
deceasedReportedDate	128838	0.01	10	62	0	207	0
deathSourceURL	128983	0.01	14	123	0	656	0

Variable type: logical

skim_variable	n_missing	complete_rate	mean	count
confirmedPatient	0	1	0.99	TRU: 128248, FAL: 1810
charterFlightPassenger	130044	0	1.00	TRU: 14
cruisePassengerDisembarked	130047	0	1.00	TRU: 11

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
ageBracket	0	1	29.16	24.5	-1	10	30	50	100	▇▇▅▂▁

　
元がJSON形式なので、読み込んだ直後は殆どの変量（フィーチャー）が文字型になっていることが分かる。また、意外と欠損が多いことも分かる。

　

Tidy & Transform

各変量（フィーチャー）を適切な形式に変換し、地域区分でも分析できるように都道府県データと結合することで、ベースとなるデータセットを作成する。なお、都道府県以外で報告されたレコードを除いている。

x <- df %>% 
  dplyr::select(patientId, date = dateAnnounced, gender,
                pref = detectedPrefecture, patientStatus, knownCluster,
                confirmedPatient, charterFlightPassenger,
                cruisePassengerDisembarked, ageBracket,
                deceasedDate, deceasedReportedDate) %>% 
  dplyr::filter(confirmedPatient == TRUE) %>% 
  dplyr::mutate(date = lubridate::as_date(date),
                gender = forcats::as_factor(gender),
                patientStatus = forcats::as_factor(patientStatus),
                cluster = dplyr::if_else(!is.na(knownCluster), TRUE, FALSE),
                ageBracket = forcats::as_factor(ageBracket),
                deceasedDate = lubridate::as_date(deceasedDate),
                deceasedReportedDate = lubridate::as_date(deceasedReportedDate)) %>% 
  dplyr::left_join(prefs, by = c("pref" = "pref")) %>% 
  dplyr::select(-`推計人口`, -pref) %>% 
  dplyr::rename(pref = `都道府県`, region = `八地方区分`) %>% 
  tidyr::drop_na(pref)

x

変換結果を要約してみると

x %>% 
  skimr::skim()

Data summary

Name	Piped data
Number of rows	126839
Number of columns	18
_______________________
Column type frequency:
character	2
Date	3
factor	9
logical	4
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
patientId	0	1.00	2	8	0	126839	0
knownCluster	124376	0.02	3	88	0	229	0

Variable type: Date

skim_variable	n_missing	complete_rate	min	max	median	n_unique
date	0	1	2020-01-15	2020-11-20	2020-08-26	294
deceasedDate	126460	0	2020-02-13	2020-11-19	2020-05-08	150
deceasedReportedDate	126510	0	2020-02-13	2020-10-17	2020-05-16	130

Variable type: factor

skim_variable	n_missing	complete_rate	ordered	n_unique	top_counts
gender	27766	0.78	FALSE	2	M: 55495, F: 43578
patientStatus	124328	0.02	FALSE	8	Hos: 1246, Dec: 371, Hom: 315, Dis: 276
ageBracket	0	1.00	FALSE	13	-1: 27851, 20: 26838, 30: 17192, 40: 14374
pcode	0	1.00	FALSE	47	13: 36816, 27: 16744, 14: 11006, 23: 8475
pref	0	1.00	FALSE	47	東京都: 36816, 大阪府: 16744, 神奈川: 11006, 愛知県: 8475
region	0	1.00	FALSE	8	関東地: 64055, 近畿地: 26388, 中部地: 13200, 九州地: 12089
広域圏	10603	0.92	FALSE	8	首都圏: 64355, 近畿圏: 25705, 中部圏: 11730, 九州圏: 8159
通俗的区分	0	1.00	FALSE	11	関東: 64055, 関西: 25705, 東海: 11164, 九州: 8159
fct_pref	0	1.00	FALSE	47	Tok: 36816, Osa: 16744, Kan: 11006, Aic: 8475

Variable type: logical

skim_variable	n_missing	complete_rate	mean	count
confirmedPatient	0	1	1.00	TRU: 126839
charterFlightPassenger	126832	0	1.00	TRU: 7
cruisePassengerDisembarked	126828	0	1.00	TRU: 11
cluster	0	1	0.02	FAL: 124376, TRU: 2463

　
文字型を因子型に変換するだけでも大まかな傾向が見えるようになる。例えば

• 年齢別で見ると20代、30代、年齢不明（恐らく非回答）、40代の順に多い
• 都道府県別では東京、大阪、神奈川、愛知の順と人口にほぼ比例
• 地方区分で見ると関東、近畿、九州、中部となっており九州地方が以外と多い

ことが読める。

patientStatusは以下の通りで、ほぼ更新されていないのと思われる。死者数などの推移を見る場合は集計データを使った方がいいことが分かる。

x %>% 
  dplyr::group_by(patientStatus) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  dplyr::mutate(Japanese = c("回復", "入院中", "退院済", "死亡", "詳細不明",
                             "重症", "自宅療養", "ホテル療養", NA))

　

Data Wrangling

陽性者の集計

最初に陽性者をキーに集計する。 　

全国集計

全国の累計陽性者数と推計人口[千人]、ならびに、人口千人あたりの累計陽性者数。

r_by_all <- x %>% 
  dplyr::filter(!is.na(pref)) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::bind_cols(prefs %>% dplyr::summarise(population = sum(`推計人口`))) %>% 
  dplyr::mutate(rate = round(n / population, 2))

r_by_all %>% 
  dplyr::rename(`累計陽性者数[人]` = n, `推計人口[千人]` = population,
                `人口千人あたりの累計陽性者数` = rate)

　

地方別集計

次に地方別の累計陽性者数と推計人口[千人]、ならびに、人口千人あたりの累計陽性者数。

region <- prefs %>% 
  dplyr::group_by(`八地方区分`) %>% 
  dplyr::summarise(population = sum(`推計人口`)) %>% 
  dplyr::rename(region = `八地方区分`)

r_by_region <- x %>% 
  dplyr::group_by(region) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  dplyr::left_join(region, by = c("region" = "region")) %>% 
  dplyr::select(region, n, population) %>% 
  dplyr::mutate(rate = round(n / population, 2))

r_by_region %>% 
  dplyr::rename(`地方` = region,
                `累計陽性者数[人]` = n, `推計人口[千人]` = population,
                `人口千人あたりの累計陽性者数` = rate)

　

上表を可視化する。グレーの破線は切片ゼロで傾きが全国の人口千人あたりの累計陽性者数（1）。

r_by_region %>% 
  dplyr::rename(key = region) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = r_by_all$rate, intercept = 0,
                         colour = "gray", linetype = "dashed") + 
    ggplot2::geom_point(ggplot2::aes(colour = key)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = key, colour = key)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("推計人口と累計陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数[人]")

　

都道府県別集計

同様に都道府県別の累計陽性者数と推計人口[千人]、ならびに、人口千人あたりの累計陽性者数。任意の列でソートできるようにしてある。

r_by_pref <- x %>% 
  dplyr::group_by(pref) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  dplyr::left_join(prefs, by = c("pref" = "都道府県")) %>% 
  dplyr::select(pref, n, population = `推計人口`) %>% 
  dplyr::mutate(rate = round(n / population, 2))

r_by_pref %>% 
  dplyr::rename(`都道府県` = pref,
                `累計陽性者数[人]` = n, `推計人口[千人]` = population,
                `人口千人あたりの累計陽性者数` = rate) %>% 
  tibble::rowid_to_column("No") %>% 
  DT::datatable()

　

上表を可視化する。グレーの破線は切片ゼロで傾きが全国の人口千人あたりの累計陽性者数（1）。

r_by_pref %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = r_by_all$rate, intercept = 0,
                         colour = "gray", linetype = "dashed") + 
    ggplot2::geom_point(ggplot2::aes(colour = key)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = key, colour = key)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("推計人口と累計陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数[人]")

　

推計人口が550万人未満の都道府県のみ抽出する。グレーの破線は上図と同様。

r_by_pref %>% 
  dplyr::filter(population < 5500) %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = r_by_all$rate, intercept = 0,
                         colour = "gray", linetype = "dashed") + 
    ggplot2::geom_point(ggplot2::aes(colour = key)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = key, colour = key)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("推計人口と累計陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数[人]")

　

クラスタ比率

全国のクラスタ比率

x %>% 
  dplyr::filter(!is.na(pref)) %>% 
  dplyr::group_by(cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
  dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1)) %>% 
  dplyr::rename(`非クラスタ感染者[人]` = `FALSE`, `クラスタ感染者[人]` = `TRUE`,
                `クラスタ比率[%]` = ratio)

地方別クラスタ比率

地方別の累計陽性者数の内、クラスタ感染と判定された人数の割合を求める。

x %>% 
  dplyr::group_by(region, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
  dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1)) %>% 
  dplyr::rename(`地方` = region,
                `非クラスタ感染者[人]` = `FALSE`, `クラスタ感染者[人]` = `TRUE`,
                `クラスタ比率[%]` = ratio)

　

都道府県別クラスタ比率

同様に都道府県別のクラスタ比率。任意の列でソートできるようにしてある。

x %>% 
  dplyr::group_by(pref, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
  dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1)) %>% 
  tidyr::replace_na(list(`TRUE` = 0L, ratio = 0.0)) %>% 
  dplyr::rename(`都道府県` = pref,
                `非クラスタ感染者[人]` = `FALSE`, `クラスタ感染者[人]` = `TRUE`,
                `クラスタ比率[%]` = ratio) %>% 
  tibble::rowid_to_column(var = "No") %>% 
  DT::datatable()

　

陽性者の日次集計

　

全国日次集計

全国の日次単位の陽性者数、前日差、累計、移動平均を求める。

x_by_all <- x %>% 
  dplyr::group_by(date) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day"),
                  fill = list(n = 0L)) %>% 
  dplyr::mutate(diff = lagdiff(n), cum = cumsum(n), ma7 = ma7(n), ma28 = ma28(n))

x_by_all %>% 
  dplyr::select(`発表日` = date, `陽性者数` = n, `前日差` = diff,
                `累計陽性者数` = cum, `移動平均（7日）` = ma7)

　

上表を可視化する。

sec_scale <- 100

x_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n), stat = "identity", width = 1.0,
                      alpha = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7), linetype = "dashed",
                       colour = "dark green", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale),
                       colour = "dark green", size = 1.0) +
    ggplot2::labs(title = paste0("【全国】陽性者数の推移（単日） @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") +
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（破線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

x_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff), colour = "dark green", alpha = 0.5) + 
    ggplot2::labs(title = paste0("【全国】陽性者数の前日差 @", datetime),
                  subtitle = subtitle, caption = caption, 
                  x = "", y = "前日差")

　

地方別日次集計

同様に地方別の日次単位の陽性者数、前日差、累計、移動平均を求める。

x_by_region <- x %>% 
  dplyr::group_by(date, region) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  tidyr::pivot_wider(names_from = region, values_from = n, values_fill = 0L) %>% 
  tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day")) %>% 
  tidyr::pivot_longer(cols = -date, names_to = "region", values_to = "n") %>% 
  tidyr::replace_na(replace = list(n = 0L)) %>% 
  dplyr::group_by(region) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n)),
                ma28 = purrr::map(data, ~ ma28(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::left_join(prefs %>% dplyr::distinct(`八地方区分`), .,
                   by = c("八地方区分" = "region")) %>% 
  dplyr::mutate(region = forcats::fct_inorder(`八地方区分`)) %>% 
  dplyr::arrange(date)

x_by_region %>% 
  dplyr::filter(date == max(date)) %>% 
  dplyr::mutate(ma7 = round(ma7, 1)) %>% 
  dplyr::select(`地方` = region,
                `発表日` = date, `陽性者数` = n, `前日差` = diff,
                `陽性者累計` = cum, `移動平均（7日）` = ma7)

x_by_region %>% 
  dplyr::select(`地方` = region,
                `発表日` = date, `陽性者数` = n, `前日差` = diff,
                `陽性者累計` = cum, `移動平均（7日）` = ma7)

　

上表を可視化する。

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date, y = n)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      width = 1.0, alpha = 0.5) + 
    ggplot2::labs(title = paste0("【地方別】陽性者数の推移（単日） @", datetime),
                  subtitle = subtitle, caption = caption, 
                  x = "", y = "陽性者数") 

　

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date, y = ma7, colour = region)) + 
    ggplot2::geom_line(size = 1) +
    ggplot2::theme(legend.position = 'none') +
    ggplot2::labs(title = paste0("【地方別】移動平均（7日） @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "陽性者数") + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region),
                             data = subset(x_by_region, date == max(date)),
                             nudge_x = 30, segment.alpha = 0.5, size = 4) + 
    ggplot2::lims(x = c(min(x_by_region$date),
                        max(x_by_region$date) + 45))

　

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date, y = cum, colour = region)) + 
    ggplot2::geom_line(size = 1) +
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("【地方別】累計陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "累計陽性者数") + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region),
                             data = subset(x_by_region, date == max(date)),
                             nudge_x = 30, segment.alpha = 0.5, size = 4) + 
    ggplot2::lims(x = c(min(x_by_region$date),
                        max(x_by_region$date) + 45))

　

地方単位で可視化。

sec_scale <- 20
ncol <- 2

x_by_region %>% 
  dplyr::rename(key = region) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.5, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "dotted", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（点線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "陽性者累計（実線）")
    )

　

傾向が見えるように縦軸をフリースケールとする。

sec_scale <- 20
ncol <- 2

x_by_region %>% 
  dplyr::rename(key = region) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.5, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "dashed", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol  = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（破線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "陽性者累計（実線）")
    )

　

都道府県別日次集計

同様に都道府県別の日次単位の陽性者数、前日差、累計、移動平均を求める。

x_by_pref <- x %>% 
  dplyr::group_by(date, pref) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  tidyr::pivot_wider(names_from = pref, values_from = n, values_fill = 0L) %>% 
  tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day")) %>% 
  tidyr::pivot_longer(cols = -date, names_to = "pref", values_to = "n") %>% 
  tidyr::replace_na(replace = list(n = 0L)) %>% 
  dplyr::group_by(pref) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n)),
                ma28 = purrr::map(data, ~ ma28(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::left_join(prefs, ., by = c("都道府県" = "pref")) %>% 
  dplyr::mutate(pref = forcats::fct_inorder(`都道府県`)) %>% 
  dplyr::arrange(date)

x_by_pref %>% 
  dplyr::filter(date == max(date)) %>% 
  dplyr::mutate(ma7 = round(ma7, 1)) %>% 
  dplyr::select(`都道府県` = pref,
                `発表日` = date, `陽性者数` = n, `前日差` = diff,
                `陽性者累計` = cum, `移動平均（7日）` = ma7) %>% 
  DT::datatable()

x_by_pref %>% 
  dplyr::select(`都道府県` = pref,
                `発表日` = date, `陽性者数` = n, `前日差` = diff,
                `陽性者累計` = cum, `移動平均（7日）` = ma7)

　

上表を可視化する。

sec_scale <- 100
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

x_by_pref %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累計陽性者数（折線）")
    )

　

傾向が見えるように縦軸をフリースケールとする。

x_by_pref %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
  ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.35, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累計陽性者数（折線）")
    )

　

死亡者の日次集計

　

都道府県別

都道府県別の日次単位の死亡者数、前日差、累計、移動平均（7日）を求める。

start <- df_s$prefectures %>% 
  dplyr::select(pref = name, date = dailyDeceasedStartDate) %>% 
  dplyr::left_join(prefs, by = c("pref" = "pref")) %>% 
  dplyr::arrange(pcode) %>% 
  tidyr::drop_na(pcode) %>% 
  dplyr::select(date, pref = `都道府県`) %>% 
  dplyr::distinct(date) %>% 
  .$date %>% lubridate::as_date()

d_by_prefs <- df_s$prefectures %>% 
  dplyr::select(deceased = dailyDeceasedCount, pref = name) %>% 
  dplyr::left_join(prefs, by = c("pref" = "pref")) %>% 
  tidyr::drop_na(pcode) %>% 
  dplyr::select(pref = `都道府県`, deceased) %>% 
  tidyr::unnest(deceased) %>% 
  tidyr::pivot_wider(names_from = pref, values_from = deceased) %>% 
  tidyr::unnest() %>% 
  dplyr::mutate(date = seq.Date(from = start, to = start + nrow(.) - 1,
                                by = "day")) %>% 
  dplyr::select(date, dplyr::everything()) %>% 
  tidyr::pivot_longer(col = -date, names_to = "pref", values_to = "n") %>% 
  dplyr::group_by(pref) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::left_join(prefs, ., by = c("都道府県" = "pref")) %>% 
  dplyr::mutate(pref = forcats::fct_inorder(`都道府県`)) %>% 
  dplyr::select(date, pref, n, diff, cum, ma7) %>% 
  dplyr::arrange(date)
d_by_prefs

sec_scale <- 100
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

d_by_prefs %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
   ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累計陽性者数（折線）")
    )

　

地方別

集計データ$regionsには死亡者数の日次データが存在しないため$prefecturesのデータから計算する。

d_by_region <- d_by_prefs %>% 
  dplyr::select(date, pref = pref, n) %>% 
  dplyr::left_join(prefs, by = c("pref" = "都道府県")) %>% 
  tidyr::drop_na(pcode) %>% 
  dplyr::group_by(date, `八地方区分`) %>% 
  dplyr::summarise(n = sum(n)) %>% 
  dplyr::ungroup() %>% 
  dplyr::rename(region = `八地方区分`) %>% 
  dplyr::group_by(region) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::arrange(date)
d_by_region

　

陽性者比率と死亡者比率

rpd_by_all <- d_by_region %>% 
  dplyr::group_by(region) %>% 
  dplyr::summarise(d = sum(n)) %>% 
  dplyr::left_join(r_by_region, ., by = c("region")) %>% 
  dplyr::select(region, positive = n, deceased = d, population) %>% 
  dplyr::select(-region) %>% 
  dplyr::summarise_all(sum) %>% 
  dplyr::mutate(p_rate = round(positive / population, 2),
                d_rate = round(deceased / positive, 2))

rpd_by_all %>% 
  dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
                `推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
                `陽性者に対する死亡者比率` = d_rate)

　

rpd_by_region <- d_by_region %>% 
  dplyr::group_by(region) %>% 
  dplyr::summarise(d = sum(n)) %>% 
  dplyr::left_join(r_by_region, ., by = c("region")) %>% 
  dplyr::select(region, positive = n, deceased = d, population, p_rate = rate) %>% 
  dplyr::mutate(d_rate = round(deceased / positive, 2))

rpd_by_region %>% 
  dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
                `推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
                `陽性者に対する死亡者比率` = d_rate)

　

rpd_by_prefs <- d_by_prefs %>% 
  dplyr::group_by(pref) %>% 
  dplyr::summarise(d = sum(n)) %>% 
  dplyr::left_join(r_by_pref, ., by = "pref") %>% 
  dplyr::select(pref, positive = n, deceased = d, population, p_rate = rate) %>% 
  dplyr::mutate(d_rate = round(deceased / positive, 2)) 

rpd_by_prefs %>% 
  dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
                `推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
                `陽性者に対する死亡者比率` = d_rate)

　

全国日次集計

都道府県別のデータから全国の日次集計を求める。

d_by_all <- d_by_prefs %>% 
  dplyr::group_by(date) %>% 
  dplyr::summarise(n = sum(n)) %>% 
  dplyr::ungroup() %>% 
  dplyr::mutate(diff = lagdiff(n), cum = cumsum(n), ma7 = ma7(n))
d_by_all

　

Visualize

前日差

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff, colour = region)) +
    ggplot2::facet_wrap(~ region, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数前日差, Free Y scale @", datetime),
                  caption = caption, x = "", y = "")

　

都道府県別

　

単日＋累計

sec_scale <- 100
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")


x_by_pref %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

x_by_pref %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
  ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
                      alpha = 0.35, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

　

前日差

x_by_pref %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数前日差, Free Y scale @", datetime),
                  x = "", y = "")

　

死亡者の日次推移

　

全国

sec_scale <- 100
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

d_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n), stat = "identity", width = 1.0,
                      alpha = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7), linetype = "dashed",
                       colour = "dark green", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale),
                       colour = "dark green", size = 1.0) +
    ggplot2::labs(title = paste0("全国の死亡者数推移（単日） @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") +
    ggplot2::scale_y_continuous(
      name = "死亡者数・移動平均（破線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

d_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff), colour = "dark green", alpha = 0.5) + 
    ggplot2::labs(title = paste0("全国の死亡者数前日差 @", datetime),
                  subtitle = subtitle, caption = caption, 
                  x = "", y = "前日差")

　

地方別

sec_scale <- 50
ncol <- 4
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")


d_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = region),
                       linetype = "solid", size = 0.2) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = region)) +
    ggplot2::facet_wrap(~ region, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

d_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = region),
                       linetype = "solid", size = 0.2) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = region)) +
    ggplot2::facet_wrap(~ region, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

　

都道府県別日次推移

sec_scale <- 10
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")


d_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数（単日）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

d_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
  ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
                      alpha = 0.35, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数（単日）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

　

比較

陽性者数と死亡者の比較。

　

全国

sec_scale <- (1 / 50)

x_by_all %>% 
  dplyr::left_join(d_by_all, by = c("date")) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n.x), stat = "identity",
                      fill = "dark green", alpha = 0.25, width = 1.0) +
    ggplot2::geom_bar(ggplot2::aes(y = n.y / sec_scale), stat = "identity",
                      fill = "dark red", alpha = 0.25, width = 1.0) +
    # ggplot2::geom_line(ggplot2::aes(y = n.x), colour = "dark green") + 
    # ggplot2::geom_line(ggplot2::aes(y = n.y / sec_scale), colour = "dark red") + 
    ggplot2::labs(title = paste0("@", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数（濃緑）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "死亡者数（濃赤）")
    )

　

地方別

sec_scale <- (1 / 10)
ncol <- 4

x_by_region %>% 
  dplyr::left_join(d_by_region, by = c("date" = "date", "region" = "region")) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n.x), stat = "identity",
                      fill = "dark green", alpha = 0.25, width = 1.0) +
    ggplot2::geom_bar(ggplot2::aes(y = n.y / sec_scale), stat = "identity",
                      fill = "dark red", alpha = 0.25, width = 1.0) +
    # ggplot2::geom_line(ggplot2::aes(y = n.x), colour = "dark green") + 
    # ggplot2::geom_line(ggplot2::aes(y = n.y / sec_scale), colour = "dark red") + 
    ggplot2::facet_wrap(~ region, ncol = ncol, scales = "free_y") + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数（濃緑）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "死亡者数（濃赤）")
    )

　

相関

　

地方区分別

r_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") + 
    ggplot2::geom_point(ggplot2::aes(colour = region)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region, colour = region)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("推計人口と陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数")

　

rpd_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) + 
    ggplot2::geom_point(ggplot2::aes(colour = region)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region, colour = region)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "陽性者数", y = "死亡者数")

　

都道府県別

r_by_pref %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") + 
    ggplot2::geom_point(ggplot2::aes(colour = pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("@", datetime), caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数")

　

r_by_pref %>% 
  dplyr::filter(n < 5000) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") + 
    ggplot2::geom_point(ggplot2::aes(colour = pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("累計陽性者数五千人未満 @", datetime),
                  caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数")

　

rpd_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) + 
    ggplot2::geom_abline(slope = rpd_by_all$d_rate, intercept = 0, colour = "gray") +
    ggplot2::geom_point(ggplot2::aes(colour = pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "陽性者数", y = "死亡者数")

　

rpd_by_prefs %>% 
  dplyr::filter(positive < 1000) %>% 
  ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) + 
    ggplot2::geom_abline(slope = rpd_by_all$d_rate, intercept = 0, colour = "gray") +
    ggplot2::geom_point(ggplot2::aes(colour = pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "陽性者数", y = "死亡者数")

　

Model

時系列（TS）分析

日本の時系列データは週単位の変動が認められるので、frequencyを7に設定して陽性者数のデータをtsオブジェクトに変換する。

ts_week <- x_by_all %>% 
  dplyr::select(n) %>% 
  ts(frequency = 7)

時系列データに変換したものをプロットすると可視化の項でプロットした棒グラフと同じような形のグラフになることが分かります。

ts_week %>% 
  plot(main = paste0("全国 @", datetime))

上記からトレンド（長期的傾向）を除いたグラフ。デフォルト指定なのでlag = 1。つまり、前日差。

ts_week %>% 
    base::diff() %>% 
  plot(main = paste0("全国 @", datetime))

トレンド、季節変動（周期変動）、非周期変動に分解した場合。frequency = 1では分解できない点に注意。

ts_week %>% 
  stats::decompose() %>% 
  plot()

トレンドを抜き出してみる。移動平均に酷似している。

ts_week %>% 
  stats::decompose() %>% 
  .$x %>% 
  plot(ylim = c(0, 1500), main = paste0("全国 @", datetime))

par(new = TRUE)

ts_week %>% 
  stats::decompose() %>% 
  .$trend %>% 
  plot(ylim = c(0, 1500), col = "dark green", lwd = 3)

　

地方別時系列分析

x_by_region %>% 
  dplyr::select(region, n) %>% 
  split(.$region) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name)
              } )

## $北海道地方
## NULL
## 
## $東北地方
## NULL
## 
## $関東地方
## NULL
## 
## $中部地方
## NULL
## 
## $近畿地方
## NULL
## 
## $中国地方
## NULL
## 
## $四国地方
## NULL
## 
## $九州地方
## NULL

oldpar <- par()
par(mfrow=c(4, 2))
x_by_region %>% 
  dplyr::select(region, n) %>% 
  split(.$region) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, .y) {
                plot(.x, main = .y, ylim = c(0, max(.x)), col = "dark gray")
                par(new = TRUE)
                stats::decompose(.x) %>% 
                  .$trend %>% 
                  plot(ylim = c(0, max(.x)), col = "dark green", lwd = 2)
              } )

## $北海道地方
## NULL
## 
## $東北地方
## NULL
## 
## $関東地方
## NULL
## 
## $中部地方
## NULL
## 
## $近畿地方
## NULL
## 
## $中国地方
## NULL
## 
## $四国地方
## NULL
## 
## $九州地方
## NULL

par(oldpar)

x_by_pref %>% 
  dplyr::select(pref, n) %>% 
  split(.$pref) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, .y) {
                plot(.x, main = .y, ylim = c(0, max(.x)), col = "dark gray")
                # plot(.x, main = region)
                par(new = TRUE)
                stats::decompose(.x) %>% 
                  .$trend %>% 
                  plot(ylim = c(0, max(.x)), col = "dark green", lwd = 2)
              } )

## $北海道
## NULL
## 
## $青森県
## NULL
## 
## $岩手県
## NULL
## 
## $宮城県
## NULL
## 
## $秋田県
## NULL
## 
## $山形県
## NULL
## 
## $福島県
## NULL
## 
## $茨城県
## NULL
## 
## $栃木県
## NULL
## 
## $群馬県
## NULL
## 
## $埼玉県
## NULL
## 
## $千葉県
## NULL
## 
## $東京都
## NULL
## 
## $神奈川県
## NULL
## 
## $新潟県
## NULL
## 
## $富山県
## NULL
## 
## $石川県
## NULL
## 
## $福井県
## NULL
## 
## $山梨県
## NULL
## 
## $長野県
## NULL
## 
## $岐阜県
## NULL
## 
## $静岡県
## NULL
## 
## $愛知県
## NULL
## 
## $三重県
## NULL
## 
## $滋賀県
## NULL
## 
## $京都府
## NULL
## 
## $大阪府
## NULL
## 
## $兵庫県
## NULL
## 
## $奈良県
## NULL
## 
## $和歌山県
## NULL
## 
## $鳥取県
## NULL
## 
## $島根県
## NULL
## 
## $岡山県
## NULL
## 
## $広島県
## NULL
## 
## $山口県
## NULL
## 
## $徳島県
## NULL
## 
## $香川県
## NULL
## 
## $愛媛県
## NULL
## 
## $高知県
## NULL
## 
## $福岡県
## NULL
## 
## $佐賀県
## NULL
## 
## $長崎県
## NULL
## 
## $熊本県
## NULL
## 
## $大分県
## NULL
## 
## $宮崎県
## NULL
## 
## $鹿児島県
## NULL
## 
## $沖縄県
## NULL

　

Infer

時系列予測（ARIMA）

ARIMA（Auto Regressive Integrated Moving Average, 自己回帰和分移動平均）モデルによる陽性者に対する予測。予測に必要なパラメータはステップワイズにより自動的に最適なものが選択される。ただし、モデル自体を評価していないので、こういうことが出来る程度の話。

　

全国

x_by_all %>% 
  dplyr::select(n) %>% 
  ts(.$n, frequency = 7) %>% 
  forecast::auto.arima() %>%  
  forecast::forecast() %>% 
  plot(main = paste0("全国 @", datetime))

　

地方別

x_by_region %>% 
  dplyr::select(region, n) %>% 
  split(.$region) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map(., forecast::auto.arima) %>% 
  purrr::map(., forecast::forecast) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name)
              } )

## $北海道地方
## $北海道地方$mean
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##  [1] 291.1729 291.9164 278.3398 288.9103 306.5357 323.3004 341.0244 343.7432
##  [9] 350.2377 353.4261 362.6003 373.2959 383.7503 393.8668
## 
## $北海道地方$lower
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%      95%
## 45.42857 280.0265 274.1259
## 45.57143 278.3840 271.2204
## 45.71429 260.6688 251.3144
## 45.85714 269.8885 259.8189
## 46.00000 286.2080 275.4471
## 46.14286 301.5687 290.0647
## 46.28571 317.7177 305.3799
## 46.42857 317.3751 303.4166
## 46.57143 321.3654 306.0814
## 46.71429 321.7013 304.9072
## 46.85714 328.6226 310.6360
## 47.00000 337.0664 317.8877
## 47.14286 345.2001 324.7929
## 47.28571 352.8957 331.2070
## 
## $北海道地方$upper
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%      95%
## 45.42857 302.3194 308.2200
## 45.57143 305.4488 312.6124
## 45.71429 296.0107 305.3652
## 45.85714 307.9322 318.0018
## 46.00000 326.8635 337.6244
## 46.14286 345.0320 356.5360
## 46.28571 364.3311 376.6690
## 46.42857 370.1114 384.0698
## 46.57143 379.1100 394.3940
## 46.71429 385.1509 401.9450
## 46.85714 396.5779 414.5646
## 47.00000 409.5255 428.7042
## 47.14286 422.3004 442.7077
## 47.28571 434.8379 456.5267
## 
## 
## $東北地方
## $東北地方$mean
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##  [1] 32.41237 32.35028 30.22113 33.29204 31.13964 32.07825 32.66866 31.57129
##  [9] 32.87250 32.27115 32.44755 32.94230 32.49324 33.03142
## 
## $東北地方$lower
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%      95%
## 45.42857 25.53980 21.90169
## 45.57143 24.97558 21.07165
## 45.71429 22.72191 18.75207
## 45.85714 25.77100 21.78959
## 46.00000 23.21799 19.02451
## 46.14286 24.13518 19.93037
## 46.28571 24.54634 20.24664
## 46.42857 23.29548 18.91454
## 46.57143 24.54133 20.13107
## 46.71429 23.73971 19.22344
## 46.85714 23.83935 19.28245
## 47.00000 24.21715 19.59833
## 47.14286 23.63210 18.94130
## 47.28571 24.08959 19.35606
## 
## $東北地方$upper
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%      95%
## 45.42857 39.28493 42.92304
## 45.57143 39.72498 43.62891
## 45.71429 37.72034 41.69018
## 45.85714 40.81309 44.79449
## 46.00000 39.06130 43.25478
## 46.14286 40.02133 44.22613
## 46.28571 40.79097 45.09067
## 46.42857 39.84709 44.22804
## 46.57143 41.20368 45.61394
## 46.71429 40.80259 45.31886
## 46.85714 41.05575 45.61265
## 47.00000 41.66746 46.28627
## 47.14286 41.35438 46.04518
## 47.28571 41.97326 46.70678
## 
## 
## $関東地方
## $関東地方$mean
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##  [1]  924.3584  745.6881  648.3541  879.5615 1004.6457 1096.6715 1066.6143
##  [8] 1033.4936  852.0363  730.5923  944.7723 1046.6863 1151.3998 1119.5702
## 
## $関東地方$lower
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%      95%
## 45.42857 858.8264 824.1358
## 45.57143 660.9691 616.1216
## 45.71429 555.2028 505.8916
## 45.85714 782.9165 731.7558
## 46.00000 904.3349 851.2336
## 46.14286 992.1355 936.7974
## 46.28571 954.4830 895.1243
## 46.42857 907.0111 840.0553
## 46.57143 712.9553 639.3303
## 46.71429 583.8243 506.1300
## 46.85714 792.6611 712.1383
## 47.00000 890.4055 807.6754
## 47.14286 990.9900 906.0742
## 47.28571 953.9072 866.2105
## 
## $関東地方$upper
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##                80%       95%
## 45.42857  989.8903 1024.5809
## 45.57143  830.4071  875.2546
## 45.71429  741.5053  790.8165
## 45.85714  976.2064 1027.3672
## 46.00000 1104.9565 1158.0579
## 46.14286 1201.2076 1256.5457
## 46.28571 1178.7456 1238.1043
## 46.42857 1159.9761 1226.9319
## 46.57143  991.1172 1064.7422
## 46.71429  877.3604  955.0547
## 46.85714 1096.8835 1177.4063
## 47.00000 1202.9672 1285.6973
## 47.14286 1311.8096 1396.7254
## 47.28571 1285.2332 1372.9299
## 
## 
## $中部地方
## $中部地方$mean
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##  [1] 331.7653 293.0152 267.1421 339.5923 372.0626 392.8472 383.1096 394.8205
##  [9] 364.9922 345.0761 400.8455 425.8398 441.8390 434.3434
## 
## $中部地方$lower
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%      95%
## 45.42857 310.0886 298.6136
## 45.57143 265.6818 251.2124
## 45.71429 235.1367 218.1940
## 45.85714 303.5148 284.4165
## 46.00000 332.3282 311.2941
## 46.14286 349.7652 326.9590
## 46.28571 336.9220 312.4718
## 46.42857 341.4867 313.2535
## 46.57143 306.3646 275.3290
## 46.71429 281.5946 247.9896
## 46.85714 332.8558 296.8643
## 47.00000 353.6229 315.3935
## 47.14286 365.6289 325.2857
## 47.28571 354.3391 311.9874
## 
## $中部地方$upper
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%      95%
## 45.42857 353.4419 364.9169
## 45.57143 320.3486 334.8180
## 45.71429 299.1476 316.0903
## 45.85714 375.6698 394.7681
## 46.00000 411.7969 432.8311
## 46.14286 435.9292 458.7354
## 46.28571 429.2972 453.7474
## 46.42857 448.1543 476.3875
## 46.57143 423.6197 454.6553
## 46.71429 408.5575 442.1625
## 46.85714 468.8351 504.8266
## 47.00000 498.0568 536.2861
## 47.14286 518.0492 558.3924
## 47.28571 514.3477 556.6994
## 
## 
## $近畿地方
## $近畿地方$mean
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##  [1] 582.4429 545.4212 363.2178 590.9720 609.6404 670.8727 691.0345 684.6447
##  [9] 656.9992 520.9415 691.0136 704.9540 750.6783 765.7338
## 
## $近畿地方$lower
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%      95%
## 45.42857 544.5543 524.4973
## 45.57143 502.3009 479.4744
## 45.71429 317.4841 293.2742
## 45.85714 542.7665 517.2480
## 46.00000 559.0836 532.3205
## 46.14286 618.0693 590.1169
## 46.28571 636.0763 606.9832
## 46.42857 614.5350 577.4211
## 46.57143 580.4981 540.0009
## 46.71429 439.9421 397.0636
## 46.85714 605.7529 560.6185
## 47.00000 615.6349 568.3523
## 47.14286 657.4775 608.1400
## 47.28571 668.8067 617.4965
## 
## $近畿地方$upper
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%      95%
## 45.42857 620.3315 640.3885
## 45.57143 588.5414 611.3679
## 45.71429 408.9514 433.1613
## 45.85714 639.1776 664.6960
## 46.00000 660.1971 686.9603
## 46.14286 723.6760 751.6285
## 46.28571 745.9928 775.0859
## 46.42857 754.7544 791.8682
## 46.57143 733.5003 773.9975
## 46.71429 601.9410 644.8195
## 46.85714 776.2744 821.4087
## 47.00000 794.2730 841.5557
## 47.14286 843.8791 893.2166
## 47.28571 862.6610 913.9712
## 
## 
## $中国地方
## $中国地方$mean
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##  [1] 32.57477 31.36281 31.36281 31.36281 31.36281 31.36281 31.36281 31.36281
##  [9] 31.36281 31.36281 31.36281 31.36281 31.36281 31.36281
## 
## $中国地方$lower
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%      95%
## 45.42857 23.19637 18.23175
## 45.57143 21.44704 16.19795
## 45.71429 21.17170 15.77685
## 45.85714 20.90360 15.36683
## 46.00000 20.64220 14.96705
## 46.14286 20.38703 14.57680
## 46.28571 20.13766 14.19542
## 46.42857 19.89370 13.82232
## 46.57143 19.65483 13.45700
## 46.71429 19.42074 13.09899
## 46.85714 19.19115 12.74786
## 47.00000 18.96581 12.40323
## 47.14286 18.74449 12.06475
## 47.28571 18.52699 11.73211
## 
## $中国地方$upper
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%      95%
## 45.42857 41.95317 46.91779
## 45.57143 41.27859 46.52768
## 45.71429 41.55393 46.94878
## 45.85714 41.82203 47.35880
## 46.00000 42.08343 47.75857
## 46.14286 42.33860 48.14883
## 46.28571 42.58797 48.53021
## 46.42857 42.83192 48.90330
## 46.57143 43.07080 49.26863
## 46.71429 43.30489 49.62664
## 46.85714 43.53448 49.97777
## 47.00000 43.75982 50.32240
## 47.14286 43.98114 50.66088
## 47.28571 44.19864 50.99352
## 
## 
## $四国地方
## $四国地方$mean
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##  [1] 13.37482 11.67436 11.67436 11.67436 11.67436 11.67436 11.67436 11.67436
##  [9] 11.67436 11.67436 11.67436 11.67436 11.67436 11.67436
## 
## $四国地方$lower
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%      95%
## 45.42857 9.754752 7.838402
## 45.57143 7.707018 5.606836
## 45.71429 7.574975 5.404894
## 45.85714 7.447055 5.209257
## 46.00000 7.322894 5.019369
## 46.14286 7.202178 4.834751
## 46.28571 7.084637 4.654986
## 46.42857 6.970031 4.479712
## 46.57143 6.858152 4.308607
## 46.71429 6.748813 4.141388
## 46.85714 6.641849 3.977801
## 47.00000 6.537112 3.817620
## 47.14286 6.434468 3.660639
## 47.28571 6.333797 3.506675
## 
## $四国地方$upper
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%      95%
## 45.42857 16.99489 18.91124
## 45.57143 15.64169 17.74188
## 45.71429 15.77374 17.94382
## 45.85714 15.90166 18.13945
## 46.00000 16.02582 18.32934
## 46.14286 16.14653 18.51396
## 46.28571 16.26407 18.69373
## 46.42857 16.37868 18.86900
## 46.57143 16.49056 19.04010
## 46.71429 16.59990 19.20732
## 46.85714 16.70686 19.37091
## 47.00000 16.81160 19.53109
## 47.14286 16.91424 19.68807
## 47.28571 17.01491 19.84204
## 
## 
## $九州地方
## $九州地方$mean
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##  [1]  83.04569  97.04315  94.43543 103.00985 117.12173 114.72672 124.54710
##  [8] 110.85869 114.52732 108.69443 118.29455 131.28254 127.51432 131.11697
## 
## $九州地方$lower
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%      95%
## 45.42857 61.05522 49.41417
## 45.57143 70.33692 56.19950
## 45.71429 64.24415 48.26184
## 45.85714 71.51303 54.83962
## 46.00000 83.57598 65.81793
## 46.14286 77.22501 57.37280
## 46.28571 84.46354 63.24458
## 46.42857 65.24043 41.09158
## 46.57143 64.97798 38.74814
## 46.71429 54.78225 26.24287
## 46.85714 61.22472 31.01378
## 47.00000 71.41834 39.72815
## 47.14286 64.29349 30.82641
## 47.28571 64.69290 29.53011
## 
## $九州地方$upper
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%      95%
## 45.42857 105.0362 116.6772
## 45.57143 123.7494 137.8868
## 45.71429 124.6267 140.6090
## 45.85714 134.5067 151.1801
## 46.00000 150.6675 168.4255
## 46.14286 152.2284 172.0806
## 46.28571 164.6307 185.8496
## 46.42857 156.4770 180.6258
## 46.57143 164.0767 190.3065
## 46.71429 162.6066 191.1460
## 46.85714 175.3644 205.5753
## 47.00000 191.1467 222.8369
## 47.14286 190.7351 224.2022
## 47.28571 197.5411 232.7038

　

都道府県別

x_by_pref %>% 
  dplyr::select(pref, n) %>% 
  split(.$pref) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map(., forecast::auto.arima) %>% 
  purrr::map(., forecast::forecast) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name)
              } )

## $北海道
## $北海道$mean
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##  [1] 291.1729 291.9164 278.3398 288.9103 306.5357 323.3004 341.0244 343.7432
##  [9] 350.2377 353.4261 362.6003 373.2959 383.7503 393.8668
## 
## $北海道$lower
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%      95%
## 45.42857 280.0265 274.1259
## 45.57143 278.3840 271.2204
## 45.71429 260.6688 251.3144
## 45.85714 269.8885 259.8189
## 46.00000 286.2080 275.4471
## 46.14286 301.5687 290.0647
## 46.28571 317.7177 305.3799
## 46.42857 317.3751 303.4166
## 46.57143 321.3654 306.0814
## 46.71429 321.7013 304.9072
## 46.85714 328.6226 310.6360
## 47.00000 337.0664 317.8877
## 47.14286 345.2001 324.7929
## 47.28571 352.8957 331.2070
## 
## $北海道$upper
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%      95%
## 45.42857 302.3194 308.2200
## 45.57143 305.4488 312.6124
## 45.71429 296.0107 305.3652
## 45.85714 307.9322 318.0018
## 46.00000 326.8635 337.6244
## 46.14286 345.0320 356.5360
## 46.28571 364.3311 376.6690
## 46.42857 370.1114 384.0698
## 46.57143 379.1100 394.3940
## 46.71429 385.1509 401.9450
## 46.85714 396.5779 414.5646
## 47.00000 409.5255 428.7042
## 47.14286 422.3004 442.7077
## 47.28571 434.8379 456.5267
## 
## 
## $青森県
## $青森県$mean
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##  [1] 0.6240284 0.2191447 0.4440541 0.3191189 0.3885194 0.3499680 0.3713829
##  [8] 0.3594871 0.3660951 0.3624244 0.3644635 0.3633308 0.3639600 0.3636105
## 
## $青森県$lower
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##                80%       95%
## 45.42857 -1.913136 -3.256230
## 45.57143 -2.758856 -4.335315
## 45.71429 -2.614505 -4.233608
## 45.85714 -2.957780 -4.692466
## 46.00000 -3.012433 -4.812788
## 46.14286 -3.211724 -5.097170
## 46.28571 -3.321572 -5.276504
## 46.42857 -3.472123 -5.500454
## 46.57143 -3.592877 -5.688630
## 46.71429 -3.723394 -5.886295
## 46.85714 -3.842511 -6.069548
## 47.00000 -3.962415 -6.252326
## 47.14286 -4.076836 -6.427652
## 47.28571 -4.189625 -6.599963
## 
## $青森県$upper
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%      95%
## 45.42857 3.161193 4.504287
## 45.57143 3.197146 4.773604
## 45.71429 3.502613 5.121716
## 45.85714 3.596018 5.330703
## 46.00000 3.789472 5.589827
## 46.14286 3.911660 5.797106
## 46.28571 4.064338 6.019270
## 46.42857 4.191097 6.219429
## 46.57143 4.325067 6.420821
## 46.71429 4.448243 6.611144
## 46.85714 4.571438 6.798475
## 47.00000 4.689077 6.978988
## 47.14286 4.804756 7.155572
## 47.28571 4.916846 7.327184
## 
## 
## $岩手県
## $岩手県$mean
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##  [1] 15.28445 15.10090 11.60731 12.59640 13.40168 14.05732 14.59112 15.02573
##  [9] 15.37957 15.66766 15.90221 16.09318 16.24865 16.37524
## 
## $岩手県$lower
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##                80%       95%
## 45.42857 14.003107 13.324807
## 45.57143 13.640473 12.867368
## 45.71429  9.993385  9.139028
## 45.85714 10.979614 10.123742
## 46.00000 11.759210 10.889738
## 46.14286 12.355195 11.454142
## 46.28571 12.794575 11.843539
## 46.42857 13.105134 12.088433
## 46.57143 13.312620 12.218442
## 46.71429 13.439143 12.259438
## 46.85714 13.502792 12.232616
## 47.00000 13.517960 12.154723
## 47.14286 13.495941 12.038743
## 47.28571 13.445544 11.894658
## 
## $岩手県$upper
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%      95%
## 45.42857 16.56579 17.24409
## 45.57143 16.56133 17.33444
## 45.71429 13.22123 14.07558
## 45.85714 14.21318 15.06905
## 46.00000 15.04416 15.91363
## 46.14286 15.75945 16.66050
## 46.28571 16.38767 17.33871
## 46.42857 16.94632 17.96303
## 46.57143 17.44652 18.54070
## 46.71429 17.89617 19.07588
## 46.85714 18.30163 19.57180
## 47.00000 18.66839 20.03163
## 47.14286 19.00136 20.45856
## 47.28571 19.30493 20.85582
## 
## 
## $宮城県
## $宮城県$mean
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##  [1] 15.11201 14.36170 16.94058 13.53387 14.31512 13.62083 14.62297 14.24347
##  [9] 14.46119 14.16984 14.31948 14.24405 14.32959 14.27611
## 
## $宮城県$lower
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##                80%      95%
## 45.42857  9.910871 7.157557
## 45.57143  9.066339 6.263146
## 45.71429 11.399169 8.465723
## 45.85714  7.983639 5.045523
## 46.00000  8.522655 5.456309
## 46.14286  7.690377 4.550982
## 46.28571  8.469700 5.212354
## 46.42857  7.968624 4.646919
## 46.57143  8.032353 4.629133
## 46.71429  7.613937 4.143453
## 46.85714  7.613957 4.064268
## 47.00000  7.405219 3.784961
## 47.14286  7.352581 3.659174
## 47.28571  7.170803 3.409483
## 
## $宮城県$upper
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%      95%
## 45.42857 20.31314 23.06646
## 45.57143 19.65705 22.46024
## 45.71429 22.48200 25.41544
## 45.85714 19.08411 22.02222
## 46.00000 20.10759 23.17393
## 46.14286 19.55129 22.69068
## 46.28571 20.77624 24.03359
## 46.42857 20.51832 23.84003
## 46.57143 20.89003 24.29325
## 46.71429 20.72574 24.19622
## 46.85714 21.02500 24.57469
## 47.00000 21.08288 24.70314
## 47.14286 21.30660 25.00001
## 47.28571 21.38141 25.14273
## 
## 
## $秋田県
## $秋田県$mean
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##  [1] 0.3970806 0.4349586 0.4349586 0.4349586 0.4349586 0.4349586 0.4349586
##  [8] 0.4349586 0.4349586 0.4349586 0.4349586 0.4349586 0.4349586 0.4349586
## 
## $秋田県$lower
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##                 80%       95%
## 45.42857 -0.8316169 -1.482050
## 45.57143 -0.8056370 -1.462369
## 45.71429 -0.8070710 -1.464562
## 45.85714 -0.8085034 -1.466752
## 46.00000 -0.8099341 -1.468940
## 46.14286 -0.8113631 -1.471126
## 46.28571 -0.8127905 -1.473309
## 46.42857 -0.8142163 -1.475490
## 46.57143 -0.8156405 -1.477668
## 46.71429 -0.8170630 -1.479843
## 46.85714 -0.8184839 -1.482016
## 47.00000 -0.8199032 -1.484187
## 47.14286 -0.8213210 -1.486355
## 47.28571 -0.8227371 -1.488521
## 
## $秋田県$upper
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%      95%
## 45.42857 1.625778 2.276211
## 45.57143 1.675554 2.332286
## 45.71429 1.676988 2.334479
## 45.85714 1.678421 2.336670
## 46.00000 1.679851 2.338858
## 46.14286 1.681280 2.341043
## 46.28571 1.682708 2.343226
## 46.42857 1.684134 2.345407
## 46.57143 1.685558 2.347585
## 46.71429 1.686980 2.349761
## 46.85714 1.688401 2.351934
## 47.00000 1.689821 2.354104
## 47.14286 1.691238 2.356272
## 47.28571 1.692654 2.358438
## 
## 
## $山形県
## $山形県$mean
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##  [1] 1.8962591 2.1090186 2.1390235 2.3093483 1.8742663 1.3299566 1.8226320
##  [8] 1.6830065 1.5583603 1.4300503 1.3006308 1.1723566 1.0471829 0.9267714
## 
## $山形県$lower
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##                   80%          95%
## 45.42857  0.903528738  0.378009009
## 45.57143  1.097705838  0.562349198
## 45.71429  1.100785112  0.551174883
## 45.85714  1.237129186  0.669530686
## 46.00000  0.762943717  0.174645053
## 46.14286  0.176528956 -0.434058744
## 46.28571  0.626096658 -0.007310878
## 46.42857  0.476963722 -0.161476770
## 46.57143  0.318931513 -0.337182441
## 46.71429  0.161319306 -0.510306306
## 46.85714  0.006753651 -0.678183559
## 47.00000 -0.142616127 -0.838720631
## 47.14286 -0.285073136 -0.990326902
## 47.28571 -0.419288783 -1.131849978
## 
## $山形県$upper
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%      95%
## 45.42857 2.888990 3.414509
## 45.57143 3.120331 3.655688
## 45.71429 3.177262 3.726872
## 45.85714 3.381567 3.949166
## 46.00000 2.985589 3.573888
## 46.14286 2.483384 3.093972
## 46.28571 3.019167 3.652575
## 46.42857 2.889049 3.527490
## 46.57143 2.797789 3.453903
## 46.71429 2.698781 3.370407
## 46.85714 2.594508 3.279445
## 47.00000 2.487329 3.183434
## 47.14286 2.379439 3.084693
## 47.28571 2.272831 2.985393
## 
## 
## $福島県
## $福島県$mean
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##  [1] 4.996419 5.514173 4.737790 4.193280 5.494259 4.573111 5.303120 4.614134
##  [9] 4.760595 4.546770 4.487178 5.485822 4.671864 5.002360
## 
## $福島県$lower
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%         95%
## 45.42857 2.454371  1.10869201
## 45.57143 2.912666  1.53551186
## 45.71429 1.969831  0.50456183
## 45.85714 1.393448 -0.08869368
## 46.00000 2.659859  1.15941813
## 46.14286 1.724151  0.21600212
## 46.28571 2.441858  0.92719814
## 46.42857 1.703956  0.16340039
## 46.57143 1.838161  0.29111824
## 46.71429 1.609172  0.05410231
## 46.85714 1.541011 -0.01859533
## 47.00000 2.531448  0.96749661
## 47.14286 1.710920  0.14349039
## 47.28571 2.035228  0.46452302
## 
## $福島県$upper
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%       95%
## 45.42857 7.538467  8.884145
## 45.57143 8.115679  9.492834
## 45.71429 7.505750  8.971019
## 45.85714 6.993112  8.475253
## 46.00000 8.328660  9.829100
## 46.14286 7.422072  8.930221
## 46.28571 8.164381  9.679041
## 46.42857 7.524312  9.064867
## 46.57143 7.683028  9.230071
## 46.71429 7.484367  9.039437
## 46.85714 7.433346  8.992952
## 47.00000 8.440197 10.004148
## 47.14286 7.632809  9.200238
## 47.28571 7.969491  9.540196
## 
## 
## $茨城県
## $茨城県$mean
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##  [1] 38.06262 34.80158 33.09751 40.60484 37.60837 35.61699 37.62044 36.91545
##  [9] 36.91545 36.91545 36.91545 36.91545 36.91545 36.91545
## 
## $茨城県$lower
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%      95%
## 45.42857 33.15521 30.55738
## 45.57143 29.61357 26.86720
## 45.71429 27.64333 24.75605
## 45.85714 34.89688 31.87526
## 46.00000 31.65744 28.50721
## 46.14286 29.43262 26.15882
## 46.28571 31.21115 27.81827
## 46.42857 30.03280 26.38935
## 46.57143 29.75749 25.96829
## 46.71429 29.49238 25.56284
## 46.85714 29.23641 25.17138
## 47.00000 28.98871 24.79255
## 47.14286 28.74852 24.42521
## 47.28571 28.51519 24.06836
## 
## $茨城県$upper
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%      95%
## 45.42857 42.97003 45.56786
## 45.57143 39.98959 42.73595
## 45.71429 38.55170 41.43897
## 45.85714 46.31281 49.33443
## 46.00000 43.55931 46.70954
## 46.14286 41.80135 45.07515
## 46.28571 44.02974 47.42261
## 46.42857 43.79809 47.44155
## 46.57143 44.07341 47.86260
## 46.71429 44.33852 48.26805
## 46.85714 44.59448 48.65951
## 47.00000 44.84218 49.03834
## 47.14286 45.08237 49.40569
## 47.28571 45.31570 49.76253
## 
## 
## $栃木県
## $栃木県$mean
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##  [1] 7.763839 5.469220 4.402275 5.416717 5.044192 7.623905 5.993457 5.828976
##  [9] 5.327174 5.196289 5.196289 5.196289 5.196289 5.196289
## 
## $栃木県$lower
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%         95%
## 45.42857 4.723402  3.11389119
## 45.57143 2.362261  0.71753648
## 45.71429 1.114119 -0.62652577
## 45.85714 2.103624  0.34977855
## 46.00000 1.706348 -0.06060028
## 46.14286 4.261492  2.48153805
## 46.28571 2.606654  0.81378815
## 46.42857 2.262148  0.37398323
## 46.57143 1.717315 -0.19362915
## 46.71429 1.526913 -0.41553773
## 46.85714 1.492883 -0.46758151
## 47.00000 1.459163 -0.51915142
## 47.14286 1.425745 -0.57026015
## 47.28571 1.392621 -0.62091988
## 
## $栃木県$upper
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##                80%       95%
## 45.42857 10.804277 12.413788
## 45.57143  8.576178 10.220903
## 45.71429  7.690431  9.431075
## 45.85714  8.729811 10.483656
## 46.00000  8.382036 10.148984
## 46.14286 10.986318 12.766272
## 46.28571  9.380260 11.173126
## 46.42857  9.395804 11.283969
## 46.57143  8.937033 10.847977
## 46.71429  8.865666 10.808116
## 46.85714  8.899695 10.860160
## 47.00000  8.933415 10.911730
## 47.14286  8.966833 10.962839
## 47.28571  8.999958 11.013498
## 
## 
## $群馬県
## $群馬県$mean
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##  [1] 14.232187 12.478457 13.069752 12.548546 11.694825 12.130646 11.972772
##  [8] 10.993339 10.207060 10.051492  9.770167  9.478763  9.188035  9.070611
## 
## $群馬県$lower
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%         95%
## 45.42857 9.613234  7.16810747
## 45.57143 7.133149  4.30351330
## 45.71429 7.492415  4.53995127
## 45.85714 6.893798  3.90035475
## 46.00000 5.723882  2.56305502
## 46.14286 6.056178  2.84054910
## 46.28571 5.779337  2.50073110
## 46.42857 4.678185  1.33514362
## 46.57143 3.744409  0.32328769
## 46.71429 3.533482  0.08305609
## 46.85714 3.182794 -0.30435052
## 47.00000 2.846033 -0.66512194
## 47.14286 2.498588 -1.04259191
## 47.28571 2.349117 -1.20902707
## 
## $群馬県$upper
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%      95%
## 45.42857 18.85114 21.29627
## 45.57143 17.82376 20.65340
## 45.71429 18.64709 21.59955
## 45.85714 18.20329 21.19674
## 46.00000 17.66577 20.82660
## 46.14286 18.20511 21.42074
## 46.28571 18.16621 21.44481
## 46.42857 17.30849 20.65154
## 46.57143 16.66971 20.09083
## 46.71429 16.56950 20.01993
## 46.85714 16.35754 19.84468
## 47.00000 16.11149 19.62265
## 47.14286 15.87748 19.41866
## 47.28571 15.79211 19.35025
## 
## 
## $埼玉県
## $埼玉県$mean
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##  [1] 110.94213  99.86787 104.04628 104.93942 121.82069 113.04379 100.93339
##  [8] 112.12626 108.67189 110.16337 108.83399 112.98276 112.50915 108.70173
## 
## $埼玉県$lower
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##                80%      95%
## 45.42857  96.99382 89.61003
## 45.57143  85.29338 77.57811
## 45.71429  88.87143 80.83834
## 45.85714  89.18707 80.84827
## 46.00000 105.51127 96.87758
## 46.14286  96.19572 87.27689
## 46.28571  83.56335 74.36821
## 46.42857  92.31466 81.82703
## 46.57143  88.01959 77.08691
## 46.71429  88.70327 77.34297
## 46.85714  86.59540 74.82301
## 47.00000  89.99205 77.82150
## 47.14286  88.79014 76.23405
## 47.28571  84.27614 71.34601
## 
## $埼玉県$upper
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%      95%
## 45.42857 124.8904 132.2742
## 45.57143 114.4424 122.1576
## 45.71429 119.2211 127.2542
## 45.85714 120.6918 129.0306
## 46.00000 138.1301 146.7638
## 46.14286 129.8919 138.8107
## 46.28571 118.3034 127.4986
## 46.42857 131.9379 142.4255
## 46.57143 129.3242 140.2569
## 46.71429 131.6235 142.9838
## 46.85714 131.0726 142.8450
## 47.00000 135.9735 148.1440
## 47.14286 136.2282 148.7842
## 47.28571 133.1273 146.0575
## 
## 
## $千葉県
## $千葉県$mean
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##  [1] 92.49686 84.42650 87.59343 88.62467 87.76023 96.50286 92.38975 94.42449
##  [9] 90.79493 93.83318 93.43591 90.90878 96.29945 94.52823
## 
## $千葉県$lower
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%      95%
## 45.42857 80.58330 74.27664
## 45.57143 71.20954 64.21291
## 45.71429 73.79002 66.48293
## 45.85714 74.25873 66.65385
## 46.00000 72.85295 64.96152
## 46.14286 81.07324 72.90530
## 46.28571 76.45490 68.01951
## 46.42857 77.07552 67.89153
## 46.57143 72.63560 63.02263
## 46.71429 75.01313 65.05039
## 46.85714 73.97756 63.67692
## 47.00000 70.83241 60.20462
## 47.14286 75.62352 64.67835
## 47.28571 73.26965 62.01604
## 
## $千葉県$upper
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##                80%      95%
## 45.42857 104.41043 110.7171
## 45.57143  97.64345 104.6401
## 45.71429 101.39684 108.7039
## 45.85714 102.99062 110.5955
## 46.00000 102.66750 110.5589
## 46.14286 111.93247 120.1004
## 46.28571 108.32459 116.7600
## 46.42857 111.77346 120.9574
## 46.57143 108.95426 118.5672
## 46.71429 112.65324 122.6160
## 46.85714 112.89427 123.1949
## 47.00000 110.98515 121.6129
## 47.14286 116.97538 127.9206
## 47.28571 115.78680 127.0404
## 
## 
## $東京都
## $東京都$mean
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##  [1] 497.2710 386.0890 328.2854 431.2748 495.7694 560.5452 536.6619 534.8058
##  [9] 428.7164 372.6483 473.6632 521.8115 588.8737 563.1013
## 
## $東京都$lower
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%      95%
## 45.42857 449.4548 424.1425
## 45.57143 330.6762 301.3425
## 45.71429 269.4185 238.2563
## 45.85714 369.2735 336.4520
## 46.00000 430.9021 396.5635
## 46.14286 493.0416 457.3073
## 46.28571 466.7215 429.6973
## 46.42857 455.8963 414.1241
## 46.57143 344.8305 300.4240
## 46.71429 285.1336 238.8061
## 46.85714 382.8018 334.7027
## 47.00000 427.8502 378.1101
## 47.14286 492.0305 440.7648
## 47.28571 463.5707 410.8823
## 
## $東京都$upper
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%      95%
## 45.42857 545.0872 570.3996
## 45.57143 441.5018 470.8355
## 45.71429 387.1523 418.3145
## 45.85714 493.2761 526.0977
## 46.00000 560.6367 594.9754
## 46.14286 628.0488 663.7830
## 46.28571 606.6024 643.6266
## 46.42857 613.7152 655.4874
## 46.57143 512.6023 557.0088
## 46.71429 460.1631 506.4906
## 46.85714 564.5246 612.6237
## 47.00000 615.7728 665.5129
## 47.14286 685.7169 736.9826
## 47.28571 662.6320 715.3203
## 
## 
## $神奈川県
## $神奈川県$mean
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##  [1] 175.5420 150.0625 130.4155 186.9330 234.7746 223.8805 215.0737 194.2427
##  [9] 182.1830 178.5934 209.1957 232.9151 224.6508 218.0339
## 
## $神奈川県$lower
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%       95%
## 45.42857 155.2541 144.51436
## 45.57143 127.0573 114.87908
## 45.71429 106.1200  93.25877
## 45.85714 162.3967 149.40801
## 46.00000 209.9315 196.78033
## 46.14286 198.5339 185.11617
## 46.28571 187.9519 173.59442
## 46.42857 161.1325 143.60493
## 46.57143 146.4895 127.59454
## 46.71429 141.5432 121.93001
## 46.85714 171.4018 151.39490
## 47.00000 194.2625 173.80099
## 47.14286 184.7363 163.60683
## 47.28571 176.2009 154.05586
## 
## $神奈川県$upper
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%      95%
## 45.42857 195.8299 206.5696
## 45.57143 173.0678 185.2460
## 45.71429 154.7109 167.5722
## 45.85714 211.4692 224.4579
## 46.00000 259.6177 272.7688
## 46.14286 249.2271 262.6448
## 46.28571 242.1955 256.5530
## 46.42857 227.3530 244.8805
## 46.57143 217.8765 236.7715
## 46.71429 215.6436 235.2568
## 46.85714 246.9895 266.9964
## 47.00000 271.5677 292.0292
## 47.14286 264.5653 285.6948
## 47.28571 259.8668 282.0119
## 
## 
## $新潟県
## $新潟県$mean
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##  [1]  0.3596202  7.0617094 17.3480339 13.2052690  3.8429348  9.5009646
##  [7]  9.3358494  6.6022803 11.9148688 10.4496802  6.0010873  9.2557024
## [13] 10.2274894  8.0444109
## 
## $新潟県$lower
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##                 80%        95%
## 45.42857 -2.3912174 -3.8474229
## 45.57143  4.2445959  2.7533060
## 45.71429 14.5268486 13.0334033
## 45.85714 10.3375778  8.8195138
## 46.00000  0.9522918 -0.5779221
## 46.14286  6.5492268  4.9866713
## 46.28571  6.0499837  4.3105514
## 46.42857  3.1914374  1.3858462
## 46.57143  8.4979539  6.6891484
## 46.71429  6.9601743  5.1129415
## 46.85714  2.4735861  0.6062397
## 47.00000  5.7053894  3.8259672
## 47.14286  6.5573030  4.6144237
## 47.28571  4.3097020  2.3326666
## 
## $新潟県$upper
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##                80%       95%
## 45.42857  3.110458  4.566663
## 45.57143  9.878823 11.370113
## 45.71429 20.169219 21.662664
## 45.85714 16.072960 17.591024
## 46.00000  6.733578  8.263792
## 46.14286 12.452702 14.015258
## 46.28571 12.621715 14.361147
## 46.42857 10.013123 11.818714
## 46.57143 15.331784 17.140589
## 46.71429 13.939186 15.786419
## 46.85714  9.528589 11.395935
## 47.00000 12.806015 14.685438
## 47.14286 13.897676 15.840555
## 47.28571 11.779120 13.756155
## 
## 
## $富山県
## $富山県$mean
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##  [1] 2.672830 3.034400 2.726025 2.677251 2.567065 2.546313 2.452744 2.395494
##  [9] 2.331418 2.281576 2.226195 2.178078 2.131402 2.088885
## 
## $富山県$lower
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##                  80%        95%
## 45.42857  0.22239412 -1.0747882
## 45.57143  0.45068403 -0.9170528
## 45.71429 -0.00645608 -1.4529442
## 45.85714 -0.16341047 -1.6671658
## 46.00000 -0.44432679 -2.0384614
## 46.14286 -0.56209812 -2.2075914
## 46.28571 -0.74477610 -2.4374410
## 46.42857 -0.87571289 -2.6073852
## 46.57143 -1.00941644 -2.7779473
## 46.71429 -1.11586264 -2.9143581
## 46.85714 -1.22107095 -3.0459434
## 47.00000 -1.31213812 -3.1597471
## 47.14286 -1.39672434 -3.2644018
## 47.28571 -1.47199740 -3.3570145
## 
## $富山県$upper
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%      95%
## 45.42857 5.123265 6.420448
## 45.57143 5.618117 6.985854
## 45.71429 5.458506 6.904994
## 45.85714 5.517913 7.021668
## 46.00000 5.578457 7.172592
## 46.14286 5.654724 7.300217
## 46.28571 5.650264 7.342929
## 46.42857 5.666701 7.398373
## 46.57143 5.672252 7.440783
## 46.71429 5.679015 7.477510
## 46.85714 5.673461 7.498334
## 47.00000 5.668295 7.515904
## 47.14286 5.659529 7.527207
## 47.28571 5.649767 7.534784
## 
## 
## $石川県
## $石川県$mean
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##  [1] 0.394226 0.394226 0.394226 0.394226 0.394226 0.394226 0.394226 0.394226
##  [9] 0.394226 0.394226 0.394226 0.394226 0.394226 0.394226
## 
## $石川県$lower
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##                80%       95%
## 45.42857 -3.255369 -5.187348
## 45.57143 -3.460450 -5.500992
## 45.71429 -3.655157 -5.798771
## 45.85714 -3.840923 -6.082875
## 46.00000 -4.018876 -6.355031
## 46.14286 -4.189926 -6.616629
## 46.28571 -4.354819 -6.868812
## 46.42857 -4.514176 -7.112527
## 46.57143 -4.668519 -7.348575
## 46.71429 -4.818295 -7.577636
## 46.85714 -4.963885 -7.800297
## 47.00000 -5.105623 -8.017066
## 47.14286 -5.243798 -8.228388
## 47.28571 -5.378667 -8.434652
## 
## $石川県$upper
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%      95%
## 45.42857 4.043821 5.975800
## 45.57143 4.248901 6.289443
## 45.71429 4.443609 6.587223
## 45.85714 4.629375 6.871327
## 46.00000 4.807328 7.143483
## 46.14286 4.978378 7.405081
## 46.28571 5.143271 7.657264
## 46.42857 5.302628 7.900979
## 46.57143 5.456971 8.137027
## 46.71429 5.606747 8.366088
## 46.85714 5.752337 8.588749
## 47.00000 5.894074 8.805518
## 47.14286 6.032250 9.016840
## 47.28571 6.167119 9.223104
## 
## 
## $福井県
## $福井県$mean
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##  [1] 5.221112 2.545686 2.998726 3.339905 1.974571 2.564055 2.248480 1.743632
##  [9] 2.061172 1.689763 1.578083 1.662244 1.416038 1.425263
## 
## $福井県$lower
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##                 80%        95%
## 45.42857  3.2606441  2.2228350
## 45.57143  0.4329516 -0.6854625
## 45.71429  0.6894425 -0.5330185
## 45.85714  0.7798866 -0.5753053
## 46.00000 -0.6313090 -2.0107785
## 46.14286 -0.1412056 -1.5732841
## 46.28571 -0.5221556 -1.9888414
## 46.42857 -1.0502018 -2.5291680
## 46.57143 -0.7761117 -2.2780789
## 46.71429 -1.1669826 -2.6792522
## 46.85714 -1.2916913 -2.8108580
## 47.00000 -1.2242530 -2.7522721
## 47.14286 -1.4773639 -3.0090380
## 47.28571 -1.4749438 -3.0102207
## 
## $福井県$upper
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%      95%
## 45.42857 7.181580 8.219389
## 45.57143 4.658421 5.776835
## 45.71429 5.308010 6.530471
## 45.85714 5.899924 7.255115
## 46.00000 4.580451 5.959920
## 46.14286 5.269316 6.701394
## 46.28571 5.019115 6.485801
## 46.42857 4.537466 6.016432
## 46.57143 4.898456 6.400423
## 46.71429 4.546508 6.058778
## 46.85714 4.447857 5.967024
## 47.00000 4.548740 6.076760
## 47.14286 4.309439 5.841113
## 47.28571 4.325471 5.860747
## 
## 
## $山梨県
## $山梨県$mean
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##  [1] 3.186443 3.550072 3.661519 3.695677 3.706145 3.709354 3.710337 3.710639
##  [9] 3.710731 3.710759 3.710768 3.710771 3.710771 3.710772
## 
## $山梨県$lower
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%         95%
## 45.42857 1.368830  0.40664431
## 45.57143 1.548541  0.48899494
## 45.71429 1.588972  0.49183216
## 45.85714 1.575709  0.45346641
## 46.00000 1.545423  0.40160643
## 46.14286 1.510256  0.34612397
## 46.28571 1.474005  0.29016165
## 46.42857 1.437826  0.23467187
## 46.57143 1.402059  0.17992134
## 46.71429 1.366789  0.12596584
## 46.85714 1.332027  0.07279778
## 47.00000 1.297762  0.02039171
## 47.14286 1.263974 -0.03128172
## 47.28571 1.230647 -0.08225163
## 
## $山梨県$upper
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%      95%
## 45.42857 5.004056 5.966242
## 45.57143 5.551603 6.611149
## 45.71429 5.734067 6.831207
## 45.85714 5.815644 6.937887
## 46.00000 5.866867 7.010684
## 46.14286 5.908452 7.072584
## 46.28571 5.946670 7.130513
## 46.42857 5.983451 7.186605
## 46.57143 6.019403 7.241541
## 46.71429 6.054730 7.295553
## 46.85714 6.089509 7.348738
## 47.00000 6.123780 7.401149
## 47.14286 6.157568 7.452825
## 47.28571 6.190896 7.503795
## 
## 
## $長野県
## $長野県$mean
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##  [1] 19.61167 24.23800 27.85840 26.37459 22.92684 22.60670 25.46301 27.32489
##  [9] 26.03038 23.93723 24.04763 25.94551 26.93046 25.96599
## 
## $長野県$lower
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%      95%
## 45.42857 16.54693 14.92455
## 45.57143 21.05687 19.37288
## 45.71429 24.53128 22.77001
## 45.85714 22.93472 21.11377
## 46.00000 19.23672 17.28329
## 46.14286 18.41805 16.20071
## 46.28571 20.77675 18.29600
## 46.42857 22.33356 19.69131
## 46.57143 20.82677 18.07214
## 46.71429 18.46672 15.57081
## 46.85714 18.21710 15.13061
## 47.00000 19.77726 16.51199
## 47.14286 20.52099 17.12802
## 47.28571 19.35779 15.85962
## 
## $長野県$upper
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%      95%
## 45.42857 22.67642 24.29880
## 45.57143 27.41913 29.10311
## 45.71429 31.18553 32.94680
## 45.85714 29.81445 31.63541
## 46.00000 26.61696 28.57039
## 46.14286 26.79535 29.01268
## 46.28571 30.14927 32.63003
## 46.42857 32.31622 34.95847
## 46.57143 31.23400 33.98863
## 46.71429 29.40775 32.30366
## 46.85714 29.87815 32.96464
## 47.00000 32.11375 35.37902
## 47.14286 33.33993 36.73289
## 47.28571 32.57419 36.07236
## 
## 
## $岐阜県
## $岐阜県$mean
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##  [1] 12.55677 14.26028 15.47006 15.49875 17.16092 17.55823 15.52190 15.12254
##  [9] 15.63604 16.16579 15.85691 16.67888 16.45286 16.12031
## 
## $岐阜県$lower
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##                80%       95%
## 45.42857  9.123750  7.306420
## 45.57143 10.334030  8.255598
## 45.71429 11.280892  9.063278
## 45.85714 11.103088  8.776167
## 46.00000 12.578812 10.153190
## 46.14286 12.799934 10.281042
## 46.28571 10.594530  7.986139
## 46.42857  9.716093  6.854090
## 46.57143  9.936673  6.919610
## 46.71429 10.223678  7.078116
## 46.85714  9.691207  6.427282
## 47.00000 10.300086  6.923354
## 47.14286  9.868608  6.383116
## 47.28571  9.337037  5.746188
## 
## $岐阜県$upper
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%      95%
## 45.42857 15.98979 17.80712
## 45.57143 18.18653 20.26496
## 45.71429 19.65924 21.87685
## 45.85714 19.89441 22.22133
## 46.00000 21.74303 24.16865
## 46.14286 22.31653 24.83542
## 46.28571 20.44927 23.05766
## 46.42857 20.52900 23.39100
## 46.57143 21.33541 24.35247
## 46.71429 22.10790 25.25346
## 46.85714 22.02261 25.28653
## 47.00000 23.05768 26.43441
## 47.14286 23.03711 26.52260
## 47.28571 22.90359 26.49444
## 
## 
## $静岡県
## $静岡県$mean
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##  [1] 58.23096 80.02593 69.45184 55.24028 79.39211 84.05299 70.98555 66.37028
##  [9] 82.49754 80.79499 68.01174 73.24584 82.64867 75.38829
## 
## $静岡県$lower
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%      95%
## 45.42857 52.10010 48.85463
## 45.57143 72.76426 68.92017
## 45.71429 61.48758 57.27156
## 45.85714 46.85625 42.41801
## 46.00000 69.75726 64.65687
## 46.14286 73.34313 67.67368
## 46.28571 59.89650 54.02632
## 46.42857 54.38557 48.04125
## 46.57143 69.30265 62.31770
## 46.71429 66.85414 59.47430
## 46.85714 53.64253 46.03592
## 47.00000 58.06283 50.02543
## 47.14286 66.54899 58.02634
## 47.28571 58.79815 50.01586
## 
## $静岡県$upper
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%       95%
## 45.42857 64.36181  67.60729
## 45.57143 87.28759  91.13168
## 45.71429 77.41610  81.63212
## 45.85714 63.62432  68.06256
## 46.00000 89.02696  94.12735
## 46.14286 94.76284 100.43229
## 46.28571 82.07460  87.94478
## 46.42857 78.35498  84.69930
## 46.57143 95.69242 102.67737
## 46.71429 94.73585 102.11569
## 46.85714 82.38096  89.98756
## 47.00000 88.42885  96.46626
## 47.14286 98.74834 107.27100
## 47.28571 91.97843 100.76071
## 
## 
## $愛知県
## $愛知県$mean
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##  [1] 210.6875 180.9596 160.1255 201.3022 203.0333 246.0702 236.6931 241.4865
##  [9] 225.0755 213.5734 236.3059 237.2615 261.0208 255.8440
## 
## $愛知県$lower
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%      95%
## 45.42857 194.4130 185.7978
## 45.57143 161.3508 150.9705
## 45.71429 136.7698 124.4060
## 45.85714 174.9917 161.0638
## 46.00000 173.9887 158.6134
## 46.14286 214.5515 197.8666
## 46.28571 202.8736 184.9707
## 46.42857 201.5510 180.4104
## 46.57143 181.1488 157.8954
## 46.71429 165.6097 140.2192
## 46.85714 184.7366 157.4375
## 47.00000 182.2870 153.1852
## 47.14286 202.8511 172.0579
## 47.28571 194.6422 162.2440
## 
## $愛知県$upper
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%      95%
## 45.42857 226.9620 235.5772
## 45.57143 200.5684 210.9486
## 45.71429 183.4812 195.8449
## 45.85714 227.6128 241.5407
## 46.00000 232.0780 247.4533
## 46.14286 277.5888 294.2738
## 46.28571 270.5125 288.4154
## 46.42857 281.4220 302.5626
## 46.57143 269.0022 292.2556
## 46.71429 261.5371 286.9275
## 46.85714 287.8751 315.1742
## 47.00000 292.2361 321.3378
## 47.14286 319.1906 349.9837
## 47.28571 317.0458 349.4441
## 
## 
## $三重県
## $三重県$mean
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##  [1] 16.27916 16.15501 15.88531 16.41732 17.30543 17.67116 17.40025 17.22627
##  [9] 17.22627 17.22627 17.22627 17.22627 17.22627 17.22627
## 
## $三重県$lower
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##                80%       95%
## 45.42857 12.385975 10.325045
## 45.57143 11.923464  9.683420
## 45.71429 11.340536  8.934676
## 45.85714 11.579548  9.018586
## 46.00000 12.191423  9.484230
## 46.14286 12.295086  9.449167
## 46.28571 11.774310  8.796119
## 46.42857 11.252396  8.090020
## 46.57143 10.984942  7.680985
## 46.71429 10.728488  7.288772
## 46.85714 10.481778  6.911462
## 47.00000 10.243780  6.547475
## 47.14286 10.013631  6.195492
## 47.28571  9.790602  5.854399
## 
## $三重県$upper
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%      95%
## 45.42857 20.17235 22.23328
## 45.57143 20.38655 22.62660
## 45.71429 20.43009 22.83595
## 45.85714 21.25509 23.81606
## 46.00000 22.41944 25.12663
## 46.14286 23.04723 25.89314
## 46.28571 23.02618 26.00437
## 46.42857 23.20014 26.36251
## 46.57143 23.46759 26.77155
## 46.71429 23.72404 27.16376
## 46.85714 23.97075 27.54107
## 47.00000 24.20875 27.90506
## 47.14286 24.43890 28.25704
## 47.28571 24.66193 28.59813
## 
## 
## $滋賀県
## $滋賀県$mean
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##  [1] 11.69719 11.68546 11.59910 11.63267 11.65248 11.71856 11.77974 11.85283
##  [9] 11.91777 11.97899 12.02919 12.07016 12.10015 12.12087
## 
## $滋賀県$lower
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%      95%
## 45.42857 7.832643 5.786876
## 45.57143 7.748090 5.663770
## 45.71429 7.226263 4.911424
## 45.85714 7.199594 4.852866
## 46.00000 7.090649 4.675762
## 46.14286 7.107295 4.666240
## 46.28571 7.094939 4.614954
## 46.42857 7.109201 4.598076
## 46.57143 7.095808 4.543214
## 46.71429 7.071982 4.474368
## 46.85714 7.017081 4.363833
## 47.00000 6.938891 4.222564
## 47.14286 6.832340 4.043728
## 47.28571 6.704000 3.836485
## 
## $滋賀県$upper
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%      95%
## 45.42857 15.56174 17.60750
## 45.57143 15.62284 17.70716
## 45.71429 15.97193 18.28677
## 45.85714 16.06574 18.41247
## 46.00000 16.21431 18.62919
## 46.14286 16.32982 18.77087
## 46.28571 16.46454 18.94453
## 46.42857 16.59646 19.10758
## 46.57143 16.73973 19.29233
## 46.71429 16.88600 19.48361
## 46.85714 17.04129 19.69454
## 47.00000 17.20142 19.91775
## 47.14286 17.36797 20.15658
## 47.28571 17.53773 20.40525
## 
## 
## $京都府
## $京都府$mean
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##  [1] 26.30983 25.94376 22.45950 29.91614 28.99258 24.09312 27.00577 26.61980
##  [9] 26.26685 25.00002 31.25151 29.58331 25.19957 26.62534
## 
## $京都府$lower
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%      95%
## 45.42857 19.45828 15.83129
## 45.57143 18.49593 14.55329
## 45.71429 14.86376 10.84282
## 45.85714 22.17531 18.07757
## 46.00000 21.10934 16.93620
## 46.14286 16.06999 11.82280
## 46.28571 18.84514 14.52516
## 46.42857 17.99928 13.43585
## 46.57143 17.40825 12.71880
## 46.71429 15.96598 11.18365
## 46.85714 22.04538 17.17194
## 47.00000 20.20824 15.24537
## 47.14286 15.65855 10.60784
## 47.28571 16.92121 11.78415
## 
## $京都府$upper
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%      95%
## 45.42857 33.16138 36.78837
## 45.57143 33.39158 37.33422
## 45.71429 30.05524 34.07618
## 45.85714 37.65696 41.75471
## 46.00000 36.87583 41.04896
## 46.14286 32.11625 36.36345
## 46.28571 35.16639 39.48637
## 46.42857 35.24032 39.80375
## 46.57143 35.12544 39.81489
## 46.71429 34.03405 38.81638
## 46.85714 40.45764 45.33107
## 47.00000 38.95838 43.92124
## 47.14286 34.74060 39.79131
## 47.28571 36.32948 41.46654
## 
## 
## $大阪府
## $大阪府$mean
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##  [1] 361.5102 331.0234 216.2591 363.0425 375.7587 399.0083 426.0042 429.8869
##  [9] 408.8837 286.6710 426.2122 434.1450 468.3342 492.9019
## 
## $大阪府$lower
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%      95%
## 45.42857 334.5705 320.3094
## 45.57143 300.8970 284.9491
## 45.71429 184.2618 167.3235
## 45.85714 329.2779 311.4040
## 46.00000 340.3148 321.5519
## 46.14286 361.9611 342.3495
## 46.28571 387.4202 366.9951
## 46.42857 384.5254 360.5124
## 46.57143 360.3266 334.6221
## 46.71429 235.5421 208.4760
## 46.85714 372.6347 344.2725
## 47.00000 378.2261 348.6244
## 47.14286 410.1680 379.3767
## 47.28571 432.5721 400.6354
## 
## $大阪府$upper
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%      95%
## 45.42857 388.4500 402.7110
## 45.57143 361.1498 377.0978
## 45.71429 248.2564 265.1947
## 45.85714 396.8071 414.6810
## 46.00000 411.2027 429.9656
## 46.14286 436.0556 455.6672
## 46.28571 464.5881 485.0132
## 46.42857 475.2484 499.2614
## 46.57143 457.4408 483.1454
## 46.71429 337.8000 364.8660
## 46.85714 479.7897 508.1519
## 47.00000 490.0640 519.6657
## 47.14286 526.5004 557.2917
## 47.28571 553.2317 585.1683
## 
## 
## $兵庫県
## $兵庫県$mean
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##  [1] 122.8203 122.4790 100.4440 140.8895 139.8124 156.4071 154.7264 151.1595
##  [9] 150.9091 134.7402 164.4184 163.6280 175.8049 174.5717
## 
## $兵庫県$lower
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##                80%      95%
## 45.42857 112.76718 107.4454
## 45.57143 111.58811 105.8228
## 45.71429  88.77527  82.5982
## 45.85714 128.49165 121.9286
## 46.00000 126.72597 119.7984
## 46.14286 142.66659 135.3928
## 46.28571 140.36155 132.7572
## 46.42857 133.94263 124.8286
## 46.57143 132.54186 122.8188
## 46.71429 115.29050 104.9945
## 46.85714 143.94339 133.1046
## 47.00000 142.17667 130.8210
## 47.14286 153.41977 141.5698
## 47.28571 151.29012 138.9656
## 
## $兵庫県$upper
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%      95%
## 45.42857 132.8734 138.1952
## 45.57143 133.3700 139.1353
## 45.71429 112.1128 118.2899
## 45.85714 153.2874 159.8505
## 46.00000 152.8989 159.8264
## 46.14286 170.1477 177.4215
## 46.28571 169.0913 176.6957
## 46.42857 168.3763 177.4904
## 46.57143 169.2763 178.9993
## 46.71429 154.1899 164.4859
## 46.85714 184.8934 195.7322
## 47.00000 185.0794 196.4350
## 47.14286 198.1901 210.0401
## 47.28571 197.8532 210.1777
## 
## 
## $奈良県
## $奈良県$mean
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##  [1] 23.00770 21.48821 21.73755 21.51266 23.50392 23.96082 22.88647 22.89066
##  [9] 22.89066 22.89066 22.89066 22.89066 22.89066 22.89066
## 
## $奈良県$lower
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%      95%
## 45.42857 18.74906 16.49467
## 45.57143 17.02288 14.65908
## 45.71429 17.07468 14.60630
## 45.85714 16.66028 14.09159
## 46.00000 18.46917 15.80394
## 46.14286 18.75007 15.99166
## 46.28571 17.50548 14.65696
## 46.42857 17.21235 14.20644
## 46.57143 17.01982 13.91199
## 46.71429 16.83340 13.62688
## 46.85714 16.65255 13.35030
## 47.00000 16.47680 13.08151
## 47.14286 16.30574 12.81989
## 47.28571 16.13901 12.56490
## 
## $奈良県$upper
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%      95%
## 45.42857 27.26634 29.52073
## 45.57143 25.95354 28.31735
## 45.71429 26.40042 28.86879
## 45.85714 26.36503 28.93372
## 46.00000 28.53867 31.20391
## 46.14286 29.17156 31.92997
## 46.28571 28.26746 31.11599
## 46.42857 28.56896 31.57488
## 46.57143 28.76150 31.86933
## 46.71429 28.94791 32.15443
## 46.85714 29.12876 32.43102
## 47.00000 29.30452 32.69981
## 47.14286 29.47558 32.96142
## 47.28571 29.64231 33.21641
## 
## 
## $和歌山県
## $和歌山県$mean
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##  [1]  9.308473 10.055625  9.735903 10.298052  9.626744  9.195049  9.393198
##  [8]  9.693723  9.474295  9.662033  9.501408  9.638836  9.521255  9.621855
## 
## $和歌山県$lower
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%      95%
## 45.42857 7.283348 6.211311
## 45.57143 7.819815 6.636249
## 45.71429 7.411798 6.181491
## 45.85714 7.804946 6.485176
## 46.00000 7.044350 5.677313
## 46.14286 6.470299 5.027904
## 46.28571 6.579595 5.090164
## 46.42857 6.808346 5.280920
## 46.57143 6.521768 4.958795
## 46.71429 6.612539 4.998235
## 46.85714 6.384269 4.734155
## 47.00000 6.433676 4.736966
## 47.14286 6.248782 4.516439
## 47.28571 6.268160 4.492821
## 
## $和歌山県$upper
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%      95%
## 45.42857 11.33360 12.40564
## 45.57143 12.29144 13.47500
## 45.71429 12.06001 13.29031
## 45.85714 12.79116 14.11093
## 46.00000 12.20914 13.57617
## 46.14286 11.91980 13.36219
## 46.28571 12.20680 13.69623
## 46.42857 12.57910 14.10653
## 46.57143 12.42682 13.98980
## 46.71429 12.71153 14.32583
## 46.85714 12.61855 14.26866
## 47.00000 12.84400 14.54071
## 47.14286 12.79373 14.52607
## 47.28571 12.97555 14.75089
## 
## 
## $鳥取県
## $鳥取県$mean
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##  [1] 0.3458131 0.2388900 0.2669715 0.3101575 0.3029407 0.3115094 0.3183052
##  [8] 0.3191324 0.3213661 0.3227888 0.3233704 0.3239579 0.3243209 0.3245277
## 
## $鳥取県$lower
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##                 80%       95%
## 45.42857 -0.5889505 -1.083784
## 45.57143 -0.7054004 -1.205278
## 45.71429 -0.6881116 -1.193702
## 45.85714 -0.6634709 -1.178879
## 46.00000 -0.6735413 -1.190460
## 46.14286 -0.6672491 -1.185373
## 46.28571 -0.6622161 -1.181273
## 46.42857 -0.6621795 -1.181655
## 46.57143 -0.6605593 -1.180359
## 46.71429 -0.6596113 -1.179663
## 46.85714 -0.6593866 -1.179627
## 47.00000 -0.6591104 -1.179515
## 47.14286 -0.6590255 -1.179578
## 47.28571 -0.6590734 -1.179760
## 
## $鳥取県$upper
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%      95%
## 45.42857 1.280577 1.775411
## 45.57143 1.183180 1.683058
## 45.71429 1.222055 1.727645
## 45.85714 1.283786 1.799194
## 46.00000 1.279423 1.796341
## 46.14286 1.290268 1.808391
## 46.28571 1.298826 1.817883
## 46.42857 1.300444 1.819920
## 46.57143 1.303292 1.823091
## 46.71429 1.305189 1.825240
## 46.85714 1.306127 1.826368
## 47.00000 1.307026 1.827431
## 47.14286 1.307667 1.828219
## 47.28571 1.308129 1.828816
## 
## 
## $島根県
## $島根県$mean
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##  [1] 0.4565916 0.4565916 0.4565916 0.4565916 0.4565916 0.4565916 0.4565916
##  [8] 0.4565916 0.4565916 0.4565916 0.4565916 0.4565916 0.4565916 0.4565916
## 
## $島根県$lower
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##                80%       95%
## 45.42857 -6.260043 -9.815615
## 45.57143 -6.260043 -9.815615
## 45.71429 -6.260043 -9.815615
## 45.85714 -6.260043 -9.815615
## 46.00000 -6.260043 -9.815615
## 46.14286 -6.260043 -9.815615
## 46.28571 -6.260043 -9.815615
## 46.42857 -6.260043 -9.815615
## 46.57143 -6.260043 -9.815615
## 46.71429 -6.260043 -9.815615
## 46.85714 -6.260043 -9.815615
## 47.00000 -6.260043 -9.815615
## 47.14286 -6.260043 -9.815615
## 47.28571 -6.260043 -9.815615
## 
## $島根県$upper
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%     95%
## 45.42857 7.173227 10.7288
## 45.57143 7.173227 10.7288
## 45.71429 7.173227 10.7288
## 45.85714 7.173227 10.7288
## 46.00000 7.173227 10.7288
## 46.14286 7.173227 10.7288
## 46.28571 7.173227 10.7288
## 46.42857 7.173227 10.7288
## 46.57143 7.173227 10.7288
## 46.71429 7.173227 10.7288
## 46.85714 7.173227 10.7288
## 47.00000 7.173227 10.7288
## 47.14286 7.173227 10.7288
## 47.28571 7.173227 10.7288
## 
## 
## $岡山県
## $岡山県$mean
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##  [1] 12.51626 13.39541 11.14474 12.48918 14.39547 12.35216 11.55274 11.61261
##  [9] 11.09931 12.72582 12.05489 10.31108 11.45729 11.69015
## 
## $岡山県$lower
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##                80%      95%
## 45.42857  9.551827 7.982553
## 45.57143 10.153980 8.438072
## 45.71429  7.798534 6.027161
## 45.85714  9.072323 7.263549
## 46.00000 10.917204 9.075922
## 46.14286  8.815694 6.943605
## 46.28571  7.959635 6.057558
## 46.42857  7.826719 5.822592
## 46.57143  7.212246 5.154558
## 46.71429  8.759171 6.659356
## 46.85714  8.015287 5.876850
## 47.00000  6.201228 4.025603
## 47.14286  7.278771 5.066795
## 47.28571  7.444184 5.196507
## 
## $岡山県$upper
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%      95%
## 45.42857 15.48068 17.04996
## 45.57143 16.63684 18.35274
## 45.71429 14.49094 16.26231
## 45.85714 15.90603 17.71481
## 46.00000 17.87373 19.71501
## 46.14286 15.88862 17.76071
## 46.28571 15.14585 17.04793
## 46.42857 15.39849 17.40262
## 46.57143 14.98638 17.04406
## 46.71429 16.69246 18.79228
## 46.85714 16.09449 18.23293
## 47.00000 14.42094 16.59656
## 47.14286 15.63581 17.84779
## 47.28571 15.93611 18.18379
## 
## 
## $広島県
## $広島県$mean
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##  [1] 8.595075 7.573456 6.413550 7.170648 7.631289 7.146957 6.999348 7.280895
##  [9] 7.303893 7.152308 7.169445 7.245380 7.221817 7.186658
## 
## $広島県$lower
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##                  80%         95%
## 45.42857  5.00202700  3.09998236
## 45.57143  3.63633044  1.55214204
## 45.71429  2.27669194  0.08677146
## 45.85714  2.40202789 -0.12232708
## 46.00000  2.32055568 -0.49077648
## 46.14286  1.54961924 -1.41343265
## 46.28571  1.08972303 -2.03864331
## 46.42857  0.98588769 -2.34648782
## 46.57143  0.68363300 -2.82092086
## 46.71429  0.25652962 -3.39387479
## 46.85714 -0.01472726 -3.81779808
## 47.00000 -0.22866041 -4.18517842
## 47.14286 -0.51721162 -4.61400565
## 47.28571 -0.80510898 -5.03569507
## 
## $広島県$upper
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%      95%
## 45.42857 12.18812 14.09017
## 45.57143 11.51058 13.59477
## 45.71429 10.55041 12.74033
## 45.85714 11.93927 14.46362
## 46.00000 12.94202 15.75335
## 46.14286 12.74430 15.70735
## 46.28571 12.90897 16.03734
## 46.42857 13.57590 16.90828
## 46.57143 13.92415 17.42871
## 46.71429 14.04809 17.69849
## 46.85714 14.35362 18.15669
## 47.00000 14.71942 18.67594
## 47.14286 14.96084 19.05764
## 47.28571 15.17843 19.40901
## 
## 
## $山口県
## $山口県$mean
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##  [1] 16.36124 15.74107 14.44691 17.12348 15.62940 17.30058 20.28944 18.43446
##  [9] 18.17593 17.05721 18.59772 17.79889 19.58445 20.16153
## 
## $山口県$lower
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%      95%
## 45.42857 13.88718 12.57749
## 45.57143 13.09513 11.69445
## 45.71429 11.63960 10.15349
## 45.85714 14.16358 12.59670
## 46.00000 12.52440 10.88072
## 46.14286 14.05697 12.33991
## 46.28571 16.91290 15.12547
## 46.42857 14.78100 12.84698
## 46.57143 14.35716 12.33562
## 46.71429 13.07999 10.97457
## 46.85714 14.46812 12.28205
## 47.00000 13.52235 11.25848
## 47.14286 15.16584 12.82677
## 47.28571 15.60528 13.19335
## 
## $山口県$upper
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%      95%
## 45.42857 18.83530 20.14499
## 45.57143 18.38700 19.78768
## 45.71429 17.25422 18.74032
## 45.85714 20.08338 21.65025
## 46.00000 18.73440 20.37808
## 46.14286 20.54419 22.26125
## 46.28571 23.66598 25.45341
## 46.42857 22.08792 24.02194
## 46.57143 21.99471 24.01624
## 46.71429 21.03443 23.13985
## 46.85714 22.72732 24.91340
## 47.00000 22.07544 24.33931
## 47.14286 24.00306 26.34213
## 47.28571 24.71778 27.12971
## 
## 
## $徳島県
## $徳島県$mean
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##  [1] 3.421452 1.839088 1.706384 1.938648 2.022698 2.023415 3.387017 2.267449
##  [9] 2.162576 2.179748 1.977102 2.052274 2.052097 1.250016
## 
## $徳島県$lower
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##                   80%        95%
## 45.42857  1.610552630  0.6519205
## 45.57143 -0.065984062 -1.0744682
## 45.71429 -0.219073111 -1.2383486
## 45.85714 -0.006980817 -1.0369346
## 46.00000  0.057104497 -0.9834179
## 46.14286  0.038057632 -1.0129272
## 46.28571  1.382090438  0.3207463
## 46.42857  0.174567877 -0.9333367
## 46.57143  0.034708986 -1.0917157
## 46.71429  0.026603578 -1.1132023
## 46.85714 -0.201026812 -1.3540587
## 47.00000 -0.150556528 -1.3166643
## 47.14286 -0.175160495 -1.3541993
## 47.28571 -1.001403847 -2.1932333
## 
## $徳島県$upper
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%      95%
## 45.42857 5.232351 6.190983
## 45.57143 3.744160 4.752644
## 45.71429 3.631841 4.651117
## 45.85714 3.884277 4.914231
## 46.00000 3.988292 5.028814
## 46.14286 4.008773 5.059758
## 46.28571 5.391944 6.453288
## 46.42857 4.360331 5.468236
## 46.57143 4.290443 5.416867
## 46.71429 4.332893 5.472699
## 46.85714 4.155231 5.308263
## 47.00000 4.255104 5.421212
## 47.14286 4.279354 5.458393
## 47.28571 3.501435 4.693264
## 
## 
## $香川県
## $香川県$mean
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##  [1] 1.037051 1.315627 1.315627 1.315627 1.315627 1.315627 1.315627 1.315627
##  [9] 1.315627 1.315627 1.315627 1.315627 1.315627 1.315627
## 
## $香川県$lower
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##                 80%        95%
## 45.42857 -0.4073738 -1.1720062
## 45.57143 -0.1869921 -0.9824307
## 45.71429 -0.1940282 -0.9931916
## 45.85714 -0.2010317 -1.0039024
## 46.00000 -0.2080030 -1.0145641
## 46.14286 -0.2149425 -1.0251772
## 46.28571 -0.2218507 -1.0357424
## 46.42857 -0.2287280 -1.0462603
## 46.57143 -0.2355748 -1.0567316
## 46.71429 -0.2423916 -1.0671569
## 46.85714 -0.2491786 -1.0775367
## 47.00000 -0.2559363 -1.0878718
## 47.14286 -0.2626651 -1.0981626
## 47.28571 -0.2693653 -1.1084097
## 
## $香川県$upper
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%      95%
## 45.42857 2.481476 3.246109
## 45.57143 2.818247 3.613685
## 45.71429 2.825283 3.624446
## 45.85714 2.832286 3.635157
## 46.00000 2.839257 3.645819
## 46.14286 2.846197 3.656432
## 46.28571 2.853105 3.666997
## 46.42857 2.859982 3.677515
## 46.57143 2.866829 3.687986
## 46.71429 2.873646 3.698411
## 46.85714 2.880433 3.708791
## 47.00000 2.887191 3.719126
## 47.14286 2.893920 3.729417
## 47.28571 2.900620 3.739664
## 
## 
## $愛媛県
## $愛媛県$mean
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##  [1] 8.256682 7.339052 7.199951 6.392877 6.293967 5.583430 5.517175 4.891007
##  [9] 4.851103 4.298735 4.279926 3.792172 3.790086 3.358954
## 
## $愛媛県$lower
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##                80%         95%
## 45.42857 6.3703753  5.37182491
## 45.57143 5.3530707  4.30175575
## 45.71429 5.0961124  3.98240761
## 45.85714 4.2236663  3.07535583
## 46.00000 4.0438372  2.85269033
## 46.14286 3.2888604  2.07418831
## 46.28571 3.1654627  1.92054166
## 46.42857 2.5084830  1.24725104
## 46.57143 2.4275372  1.14457920
## 46.71429 1.8535551  0.55915501
## 46.85714 1.8049395  0.49476064
## 47.00000 1.3019140 -0.01634912
## 47.14286 1.2780135 -0.05179737
## 47.28571 0.8360451 -0.49950246
## 
## $愛媛県$upper
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##                80%       95%
## 45.42857 10.142988 11.141539
## 45.57143  9.325033 10.376348
## 45.71429  9.303789 10.417494
## 45.85714  8.562087  9.710397
## 46.00000  8.544097  9.735244
## 46.14286  7.878001  9.092673
## 46.28571  7.868886  9.113807
## 46.42857  7.273531  8.534763
## 46.57143  7.274668  8.557626
## 46.71429  6.743915  8.038315
## 46.85714  6.754913  8.065092
## 47.00000  6.282431  7.600694
## 47.14286  6.302159  7.631970
## 47.28571  5.881864  7.217411
## 
## 
## $高知県
## $高知県$mean
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##  [1] 0.1404218 0.1627840 0.1834634 0.2025865 0.2202705 0.2366237 0.2517463
##  [8] 0.2657308 0.2786630 0.2906219 0.3016809 0.3119076 0.3213647 0.3301102
## 
## $高知県$lower
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##                80%       95%
## 45.42857 -1.540708 -2.430645
## 45.57143 -1.539926 -2.441286
## 45.71429 -1.537486 -2.448502
## 45.85714 -1.533809 -2.453001
## 46.00000 -1.529225 -2.455352
## 46.14286 -1.523997 -2.456013
## 46.28571 -1.518333 -2.455356
## 46.42857 -1.512397 -2.453681
## 46.57143 -1.506319 -2.451231
## 46.71429 -1.500200 -2.448204
## 46.85714 -1.494121 -2.444761
## 47.00000 -1.488141 -2.441029
## 47.14286 -1.482308 -2.437115
## 47.28571 -1.476656 -2.433100
## 
## $高知県$upper
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%      95%
## 45.42857 1.821552 2.711488
## 45.57143 1.865494 2.766854
## 45.71429 1.904413 2.815428
## 45.85714 1.938982 2.858174
## 46.00000 1.969766 2.895893
## 46.14286 1.997244 2.929261
## 46.28571 2.021826 2.958849
## 46.42857 2.043859 2.985143
## 46.57143 2.063645 3.008557
## 46.71429 2.081444 3.029448
## 46.85714 2.097482 3.048122
## 47.00000 2.111956 3.064845
## 47.14286 2.125038 3.079844
## 47.28571 2.136876 3.093321
## 
## 
## $福岡県
## $福岡県$mean
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##  [1] 24.77826 24.35827 21.59529 24.75551 28.34657 27.48456 32.17286 27.44275
##  [9] 27.64929 26.28701 28.50634 31.02823 30.42286 33.71532
## 
## $福岡県$lower
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##                 80%        95%
## 45.42857 11.0138327   3.727384
## 45.57143  8.1229767  -0.471467
## 45.71429  3.5561479  -5.993195
## 45.85714  5.6387708  -4.481020
## 46.00000  8.2098157  -2.449939
## 46.14286  6.3770181  -4.796637
## 46.28571 10.1372712  -1.527667
## 46.42857  2.9877538  -9.957937
## 46.57143  1.5895549 -12.205635
## 46.71429 -1.2093180 -15.764995
## 46.85714 -0.2137450 -15.417239
## 47.00000  1.1344468 -14.690367
## 47.14286 -0.6002481 -17.022892
## 47.28571  1.6025707 -15.396891
## 
## $福岡県$upper
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%      95%
## 45.42857 38.54269 45.82914
## 45.57143 40.59356 49.18800
## 45.71429 39.63442 49.18377
## 45.85714 43.87225 53.99204
## 46.00000 48.48333 59.14308
## 46.14286 48.59209 59.76575
## 46.28571 54.20846 65.87339
## 46.42857 51.89774 64.84343
## 46.57143 53.70902 67.50421
## 46.71429 53.78334 68.33902
## 46.85714 57.22642 72.42992
## 47.00000 60.92202 76.74683
## 47.14286 61.44598 77.86862
## 47.28571 65.82807 82.82753
## 
## 
## $佐賀県
## $佐賀県$mean
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##  [1] 2.135929 2.135929 2.135929 2.135929 2.135929 2.135929 2.135929 2.135929
##  [9] 2.135929 2.135929 2.135929 2.135929 2.135929 2.135929
## 
## $佐賀県$lower
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##                  80%        95%
## 45.42857  0.10937391 -0.9634193
## 45.57143  0.03596142 -1.0756941
## 45.71429 -0.03496991 -1.1841742
## 45.85714 -0.10365586 -1.2892203
## 46.00000 -0.17029706 -1.3911392
## 46.14286 -0.23506592 -1.4901946
## 46.28571 -0.29811191 -1.5866151
## 46.42857 -0.35956563 -1.6806005
## 46.57143 -0.41954193 -1.7723263
## 46.71429 -0.47814253 -1.8619482
## 46.85714 -0.53545794 -1.9496046
## 47.00000 -0.59156921 -2.0354193
## 47.14286 -0.64654916 -2.1195039
## 47.28571 -0.70046360 -2.2019589
## 
## $佐賀県$upper
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%      95%
## 45.42857 4.162483 5.235277
## 45.57143 4.235896 5.347551
## 45.71429 4.306827 5.456031
## 45.85714 4.375513 5.561077
## 46.00000 4.442154 5.662996
## 46.14286 4.506923 5.762052
## 46.28571 4.569969 5.858472
## 46.42857 4.631423 5.952458
## 46.57143 4.691399 6.044184
## 46.71429 4.750000 6.133805
## 46.85714 4.807315 6.221462
## 47.00000 4.863426 6.307277
## 47.14286 4.918406 6.391361
## 47.28571 4.972321 6.473816
## 
## 
## $長崎県
## $長崎県$mean
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##  [1] 1.659289 2.317960 1.908051 2.210948 2.153023 2.140011 2.518719 2.302129
##  [9] 2.515494 2.345913 2.496165 2.485703 2.479161 2.687544
## 
## $長崎県$lower
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##                  80%       95%
## 45.42857 -0.45680264 -1.576994
## 45.57143 -0.03547248 -1.281305
## 45.71429 -0.93071892 -2.433473
## 45.85714 -0.91186937 -2.564989
## 46.00000 -1.28924069 -3.111465
## 46.14286 -1.56589063 -3.527676
## 46.28571 -1.44687934 -3.546140
## 46.42857 -2.05026510 -4.354284
## 46.57143 -2.12081766 -4.575133
## 46.71429 -2.59162814 -5.205405
## 46.85714 -2.70867554 -5.463952
## 47.00000 -2.98142481 -5.875547
## 47.14286 -3.23426334 -6.258768
## 47.28571 -3.26394376 -6.414471
## 
## $長崎県$upper
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%       95%
## 45.42857 3.775381  4.895572
## 45.57143 4.671393  5.917225
## 45.71429 4.746822  6.249576
## 45.85714 5.333765  6.986885
## 46.00000 5.595286  7.417510
## 46.14286 5.845912  7.807698
## 46.28571 6.484317  8.583578
## 46.42857 6.654524  8.958542
## 46.57143 7.151806  9.606122
## 46.71429 7.283455  9.897231
## 46.85714 7.701006 10.456282
## 47.00000 7.952830 10.846953
## 47.14286 8.192586 11.217090
## 47.28571 8.639031 11.789559
## 
## 
## $熊本県
## $熊本県$mean
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##  [1] 5.106531 6.779354 6.158778 6.116047 6.077099 6.041599 6.009241 5.979747
##  [9] 5.952864 5.928361 5.906027 5.885669 5.867114 5.850201
## 
## $熊本県$lower
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##                 80%       95%
## 45.42857  0.8356702 -1.425187
## 45.57143  1.2364537 -1.697781
## 45.71429  0.5154822 -2.471898
## 45.85714  0.1787514 -2.964263
## 46.00000 -0.1033550 -3.375090
## 46.14286 -0.3424254 -3.721924
## 46.28571 -0.5469091 -4.017525
## 46.42857 -0.7231753 -4.271488
## 46.57143 -0.8761473 -4.491208
## 46.71429 -1.0097046 -4.682495
## 46.85714 -1.1269514 -4.849985
## 47.00000 -1.2304024 -4.997423
## 47.14286 -1.3221160 -5.127865
## 47.28571 -1.4037915 -5.243823
## 
## $熊本県$upper
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##                80%      95%
## 45.42857  9.377393 11.63825
## 45.57143 12.322255 15.25649
## 45.71429 11.802074 14.78945
## 45.85714 12.053344 15.19636
## 46.00000 12.257554 15.52929
## 46.14286 12.425623 15.80512
## 46.28571 12.565391 16.03601
## 46.42857 12.682669 16.23098
## 46.57143 12.781876 16.39694
## 46.71429 12.866426 16.53922
## 46.85714 12.939005 16.66204
## 47.00000 13.001741 16.76876
## 47.14286 13.056344 16.86209
## 47.28571 13.104194 16.94423
## 
## 
## $大分県
## $大分県$mean
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##  [1] 9.306315 7.904844 6.734843 5.758083 4.942647 4.261891 3.693570 3.219114
##  [9] 2.823021 2.492348 2.216289 1.985825 1.793425 1.632803
## 
## $大分県$lower
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##                 80%        95%
## 45.42857  7.6876590  6.8307944
## 45.57143  5.9383927  4.8974164
## 45.71429  4.5586197  3.4065966
## 45.85714  3.4468897  2.2234178
## 46.00000  2.5418690  1.2709738
## 46.14286  1.8006037  0.4976769
## 46.28571  1.1909758 -0.1338175
## 46.42857  0.6881299 -0.6516923
## 46.57143  0.2724368 -1.0777610
## 46.71429 -0.0718082 -1.4291904
## 46.85714 -0.3572832 -1.7196502
## 47.00000 -0.5942897 -1.9601201
## 47.14286 -0.7912397 -2.1594787
## 47.28571 -0.9550288 -2.3249440
## 
## $大分県$upper
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##                80%       95%
## 45.42857 10.924971 11.781836
## 45.57143  9.871295 10.912271
## 45.71429  8.911067 10.063090
## 45.85714  8.069276  9.292748
## 46.00000  7.343425  8.614321
## 46.14286  6.723178  8.026105
## 46.28571  6.196164  7.520957
## 46.42857  5.750099  7.089921
## 46.57143  5.373606  6.723804
## 46.71429  5.056504  6.413886
## 46.85714  4.789862  6.152229
## 47.00000  4.565940  5.931771
## 47.14286  4.378091  5.746330
## 47.28571  4.220634  5.590550
## 
## 
## $宮崎県
## $宮崎県$mean
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##  [1]  6.625300  8.323199  8.634562  9.481898  8.753355 10.345634  8.864282
##  [8]  9.110368  8.232583  7.765988  7.568676  7.684574  8.008026  8.357078
## 
## $宮崎県$lower
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%        95%
## 45.42857 3.952870  2.5381705
## 45.57143 5.579498  4.1270701
## 45.71429 5.554013  3.9232696
## 45.85714 6.139710  4.3704620
## 46.00000 5.130059  3.2120022
## 46.14286 6.373495  4.2707715
## 46.28571 4.479598  2.1584868
## 46.42857 4.467344  2.0094761
## 46.57143 3.231200  0.5836286
## 46.71429 2.515477 -0.2639764
## 46.85714 2.120597 -0.7634413
## 47.00000 2.078941 -0.8885020
## 47.14286 2.263666 -0.7772144
## 47.28571 2.472503 -0.6426036
## 
## $宮崎県$upper
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##                80%      95%
## 45.42857  9.297731 10.71243
## 45.57143 11.066901 12.51933
## 45.71429 11.715110 13.34585
## 45.85714 12.824086 14.59333
## 46.00000 12.376651 14.29471
## 46.14286 14.317773 16.42050
## 46.28571 13.248966 15.57008
## 46.42857 13.753391 16.21126
## 46.57143 13.233965 15.88154
## 46.71429 13.016500 15.79595
## 46.85714 13.016754 15.90079
## 47.00000 13.290207 16.25765
## 47.14286 13.752386 16.79327
## 47.28571 14.241654 17.35676
## 
## 
## $鹿児島県
## $鹿児島県$mean
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##  [1] 1.474875 2.331473 2.828979 3.117928 3.285747 3.383216 3.439825 3.472703
##  [9] 3.491799 3.502889 3.509331 3.513072 3.515244 3.516506
## 
## $鹿児島県$lower
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##                80%       95%
## 45.42857 -2.629349 -4.801993
## 45.57143 -2.482772 -5.031278
## 45.71429 -2.242358 -4.926961
## 45.85714 -2.062719 -4.805188
## 46.00000 -1.948554 -4.719425
## 46.14286 -1.881690 -4.668764
## 46.28571 -1.845307 -4.643086
## 46.42857 -1.827618 -4.633438
## 46.57143 -1.821116 -4.633603
## 46.71429 -1.821217 -4.639628
## 46.85714 -1.825185 -4.649107
## 47.00000 -1.831405 -4.660600
## 47.14286 -1.838931 -4.673261
## 47.28571 -1.847211 -4.686591
## 
## $鹿児島県$upper
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%       95%
## 45.42857 5.579098  7.751742
## 45.57143 7.145717  9.694224
## 45.71429 7.900316 10.584920
## 45.85714 8.298575 11.041044
## 46.00000 8.520049 11.290920
## 46.14286 8.648122 11.435195
## 46.28571 8.724957 11.522736
## 46.42857 8.773025 11.578845
## 46.57143 8.804713 11.617200
## 46.71429 8.826995 11.645407
## 46.85714 8.843846 11.667768
## 47.00000 8.857548 11.686744
## 47.14286 8.869420 11.703750
## 47.28571 8.880224 11.719604
## 
## 
## $沖縄県
## $沖縄県$mean
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##  [1] 38.71925 43.36422 39.48199 38.52660 41.76074 45.42560 40.93808 41.93459
##  [9] 41.93459 41.93459 41.93459 41.93459 41.93459 41.93459
## 
## $沖縄県$lower
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%       95%
## 45.42857 26.80049 20.491079
## 45.57143 30.40082 23.538413
## 45.71429 25.55208 18.178028
## 45.85714 23.69302 15.840596
## 46.00000 26.07547 17.772185
## 46.14286 28.93256 20.201663
## 46.28571 23.67502 14.536507
## 46.42857 22.99528 12.969408
## 46.57143 21.99076 11.433133
## 46.71429 21.03447  9.970610
## 46.85714 20.12006  8.572140
## 47.00000 19.24246  7.229976
## 47.14286 18.39757  5.937822
## 47.28571 17.58197  4.690471
## 
## $沖縄県$upper
## Time Series:
## Start = c(45, 4) 
## End = c(47, 3) 
## Frequency = 7 
##               80%      95%
## 45.42857 50.63801 56.94743
## 45.57143 56.32761 63.19002
## 45.71429 53.41190 60.78594
## 45.85714 53.36018 61.21261
## 46.00000 57.44602 65.74930
## 46.14286 61.91865 70.64954
## 46.28571 58.20115 67.33966
## 46.42857 60.87391 70.89978
## 46.57143 61.87843 72.43606
## 46.71429 62.83472 73.89858
## 46.85714 63.74913 75.29705
## 47.00000 64.62673 76.63921
## 47.14286 65.47162 77.93137
## 47.28571 66.28722 79.17872